Research Article  Open Access
Nengjian Wang, Chunping Ren, Chunsheng Liu, "A Novel Fractional Tikhonov Regularization Coupled with an Improved SuperMemory Gradient Method and Application to Dynamic Force Identification Problems", Mathematical Problems in Engineering, vol. 2018, Article ID 4790950, 16 pages, 2018. https://doi.org/10.1155/2018/4790950
A Novel Fractional Tikhonov Regularization Coupled with an Improved SuperMemory Gradient Method and Application to Dynamic Force Identification Problems
Abstract
This paper presents a novel inverse technique to provide a stable optimal solution for the illposed dynamic force identification problems. Due to illposedness of the inverse problems, conventional numerical approach for solutions results in arbitrarily large errors in solution. However, in the field of engineering mathematics, there are famous mathematical algorithms to tackle the illposed problem, which are known as regularization techniques. In the current study, a novel fractional Tikhonov regularization (NFTR) method is proposed to perform an effective inverse identification, then the smoothing functional of the illposed problem processed by the proposed method is regarded as an optimization problem, and finally a stable optimal solution is obtained by using an improved supermemory gradient (ISMG) method. The result of the present method is compared with that of the standard TR method and FTR method; new findings can be obtained; that is, the present method can improve accuracy and stability of the inverse identification problem, robustness is stronger, and the rate of convergence is faster. The applicability and efficiency of the present method in this paper are demonstrated through a mathematical example and an engineering example.
1. Introduction
In this paper, we present a novel regularization technique to solve the following linear operator equations of the form:where , and represent the identified vector, the measured vector, and a compact linear operator, respectively;
Under these circumstances, a generalized solution of (1) can be obtainedwhere is the Moore–Penrose pseudoinverse of which is usually an illconditioned operator, as, for example, it could be if , which causes (2) to be a typical illposed problem; see [1, 2].
Illposed problem exists widely in many fields of science and has important application in engineering [3]. However, in many cases of linear equation which appears in practical engineering problem, generally, it is clear that the measured vector is rarely obtained exactly. Conversely, noises are contained in owing to the measurement error. Consequently, when is severely illconditioned and is disturbed through noise, we will be confronted with a problem in which the generalized solution of an illposed linear equation may be seriously deviated from the exact solution [4–6].
In mathematical theory, there are several typical regularization methods which can solve the illposed problem in practical engineering applications. For example, the standard Tikhonov regularization (TR) method, as a traditional technique, has been widely adopted for overcoming the illposed nature (or numerical instability), such as system parameter identification, load source identification problem, and so on [7–10]. T. S. Jang et al. [11] applied the standard TR method to identify the loading source of infinite beams on an elastic foundation from given information of vertical deflection of infinite beams. J. Gao et al. [12] utilized the standard TR method for identifying the unknown load source. V. Melicher et al. [13] identified the piecewise constant parameter using the standard TR method. In addition, many works are mainly concerned with the identification of load position and load number in nonlinear system using the standard TR method [14–16]. However, the standard TR method is not completely perfect; there are some unavoidable limitations and disadvantages: (i) the standard TR method may determine approximate solutions that are too smooth; (ii) the approximate solution may lack many details that the desired exact solution might possess; therefore, it is very necessary to discuss other regularization methods to improve the limitations and disadvantages of the regularization method mentioned above.
In the past few years, fractional Tikhonov regularization (FTR) method was gradually introduced to deal with the illposed problems mentioned above [17]. Furthermore, the FTR method has been applied to many fields and obtained some remarkable achievements [18–20]. M. E. Hochstenbach et al. [21] used the FTR method to obtain approximate solution of higher quality which is better than the standard TR method in solving linear system equation with a severe illposedness. In [22], we can see that the FTR method is significantly better than the standard TR method, which was proved by several numerical examples. It can be found from [23] that the FTR method can obtain more approximation solutions than the standard TR method, which was discussed through several computed examples. J. Zhang et al. [24] illustrated that the FTR method was more suitable for tackling partly textured images problems. Hence, the FTR method mentioned above has many advantages, to a certain extent, which may not only solve the shortcomings of the standard TR method, but also be able to provide a useful help for obtaining the effective solution of the illposed problem. However, FTR method is also not very perfect, which has great limitations for solving the illposed problem. First, the solutions of the larger singular values can not be retained well. Then it is not completely effective for the largescale illposed problem. Thus, it is very necessary to discuss an effective technique to tackle the limitations of the problem encountered.
Considering the limitations of TR method and FTR method in a certain case, we propose a novel fractional Tikhonov regularization (NFTR) method to offer a stable solution for the illposed problems in practical engineering applications. In our work, the illposed problem processed by the proposed method is redefined as a class of unconstrained optimization problems to solve the following minimum problem: In which : is a continuously differentiable function, and denotes the smoothing functional using the NFTR method.
In fact, there are many optimization algorithms that can improve the efficiency of solving (3), like conjugate gradient (CG) method, memory gradient (MG) method, supermemory gradient (SMG) method, and so on [25–27]. It is generally known that CG method does not achieve global convergence for common functions under inexact line searches [28], whereas MG method requires dealing with the trust region subproblem [29]. Although SMG method is superior to CG and MG method, in some cases, it is also not globally convergent [30]. Therefore, it is very meaningful to find a global convergence algorithm based on the SMG method. Hence, in this paper, an improved supermemory gradient (ISMG) method is introduced to deal with unconstrained optimization problems of (3).
This article is focused on a novel stable inverse technique developed to provide an optimal solution for the illposed problems in practical engineering. Compared with our previous works, some highlights and advances of the proposed technique are derived in this article.
First of all, the article is different from our previous studies, and the highlights of this article are described as follows: (i) a novel fractional Tikhonov regularization (NFTR) coupled with an improved supermemory gradient (ISMG) method is proposed; (ii) solving the illposed problem procedure is regarded as a class of unconstrained optimization problems; (iii) the detailed information of the solution is clearly enhanced; (iv) the technique we propose can effectively overcome the smoothness of the solution of the illposed problem; (v) global convergence and stability of the solution are proved.
Then, some advances of the proposed technique are described as follows: (i) a stable optimal solution for the illposed dynamic force identification problems can be obtained; (ii) the proposed method yields approximate solutions of higher quality than the standard TR method and FTR method; (iii) the technique proposed does not need any a priori information on the model of the inverse identification; only the measured response is sufficient for the present method; (iv) the applicability of the present technique is examined through a mathematical example and an engineering example.
In this paper we propose a novel fractional Tikhonov regularization (NFTR) coupled with an improved supermemory gradient (ISMG) method to solve the illposed dynamic force identification problems in engineering applications. The remainder of this paper is formulated as follows. The inverse problem model is described in Section 2. In Section 3, we present the NFTR method for solving the illposed problem. In Section 4, ISMG method is introduced to deal with an optimization problem. The convergence properties of the present method are proved in Section 5. In Section 6, we discuss the application of a mathematical and an engineering example. Section 7 shows some meaningful findings and new conclusions.
2. Description of Inverse Problem Model
The model of inverse problems encountered in science and engineering can be uniformly expressed by a Fredholm integral equation of the first kind [31–33]: where , , and represent the identified object, the kernel function, and the measured response, respectively.
The discretization form of (4) is processed based on the rectangular formula in which is time interval; we consider thatThen we haveHence, (5) can be simply written as The vector represents available data, which is contaminated by an error . The error may stem from measurement inaccuracies or discretization. Thus, where is the unknown errorfree vector associated with . We will assume the unavailable errorfree systemto be consistent and denote its solution of minimal Euclidean norm by . We would like to determine an approximation of by computing a suitable approximate solution of (10). Due to the illconditioning of the matrix and the error in , the solution of the leastsquares problem (10) of minimal Euclidean norm is typically a poor approximation of , and a small perturbation in may give rise to an arbitrarily large perturbation in , or even make the problem unsolvable. Moreover, the righthand side function that is available in applications represents data that is contaminated by noise. Thus, instead of , the errorcontaminated function is available [34, 35]. Therefore, it is very necessary to find an effective method to solve the illposed problem.
3. The Regularization Method for Solving The IllPosed Problem
In some instances, we all know that some traditional numerical methods are not able to solve (8) effectively. However, the regularization method, as a way to deal with the illposed problem, has been used by mathematical workers and engineering technicians [36–38]. In the past few years, the standard Tikhonov regularization (TR) and fractional Tikhonov regularization (FTR) method have been successfully applied to many engineering fields. Although many meaningful achievements have been made, there are still many limitations. Hence, in this work, considering the disadvantages of the standard TR method and FTR method, a novel fractional Tikhonov regularization (NFTR) method is discussed.
3.1. The Standard Tikhonov Regularization (TR) Method
The standard Tikhonov regularization (TR) method is a popular approach to determine an approximation of x; see, e.g., [39–41] for properties and applications. This method replaces the minimization problem (8) by a penalized leastsquares problem of the following form [42]:in which , and , ,, and represent the regularization matrix, the regularization parameter, and the unit matrix, respectively.
3.2. Fractional Tikhonov Regularization (FTR) Method
It is well known that Tikhonov regularization in standard form typically determines a regularized solution that is too smooth; i.e., many details of the desired solution are not represented by the regularized solution. This shortcoming led Klann and Ramlau to introduce the fractional Tikhonov regularization method [17]. Subsequently another approach, also referred to as fractional Tikhonov regularization (FTR), was investigated in [21], the model of which is described as follows:Then we know In which is defined with the aid of the Moore–Penrose pseudoinverse of . where denotes fractional order and . denotes the transpose of . It is obvious that FTR method is different than that of TR method; it mainly considers the influence of fractional order on the identified objects, which thus may be more suitable for solving the illposed identification problem. However, FTR method is not perfect, which has great limitations for solving the illposed problem. First, the solutions of the larger singular values can not be retained well. Then it is not completely effective for solving the largescale illposed problem. Thus, it is very necessary to find an effective method to solve the limitations of problem we encountered.
3.3. A Novel Fractional Tikhonov Regularization (NFTR) Method
From Sections 3.1 and 3.2, on the one hand, we can find that the regular solution is obtained by using the standard TR method that may be too smooth and may lose some details of the solution we expected. On the other hand, the solutions of the larger singular values can not be retained perfectly using the FTR method for dealing with some practical complex engineering applications. Hence, in order to solve the limitations of TR method and FTR method in a certain case, in this paper, a novel fractional Tikhonov regularization (NFTR) method is proposed. The model of the proposed method is described as follows: in which where , , , , and represent the regularization matrix, the diagonal matrix, right column orthogonal matrix of singular value decomposition of , adjustment coefficient matrix, and symmetric positive semidefinite matrix, respectively. In this work, Γ is arranged as the identity matrix; that is, , in which can be described as follows:Hence, the NFTR method corresponding to the normal equation can be expressed in the following form:where denotes the transpose of .
Introduce the singular value decomposition (SVD) [43]:in which and
are orthogonal matrices, andThe singular values are ordered according toin which the index is the rank of ; see, e.g., [43] for discussions on properties and the computation of the SVD.
Combining (18) and (19), then we haveHence, we can obtain the regular solution of (22):Meanwhile, the filter factor of TR, FTR, and NFTR method can be expressed as follows:
3.4. Stability Analysis
In the paper, we may determine a suitable regular parameter by the discrepancy principle [44, 45]; we choose > 0 so that where is equivalent to the noise level , which satisfies , is the noise vector. is a usersupplied constant independent of , and .
Using (23) and (24), we haveFinding the partial derivative of and in (26), respectively, then
We can find from (27) that is a monotonous decreasing function. When , then , so we can obtain , also a monotonous decreasing function.
However, it is clearly seen from (27) that the regularization parameter becomes large and the norm of regular solution becomes small. Due to being a monotonous decreasing function, so reducing can increase the norm of regular solution according to .
Theorem 1. For , the regularization parameter meets (25), and then one has
Proof. The following form can be found:Then, the following form is obtained by substituting (24) and (25) into (29).Offering the derivative of We can see from the previous formula that ; then Hence, the conclusion is proved.
In this paper, the NFTR method is of order optimal when the regularization parameter is chosen according to the discrepancy principle, and the proof process is shown as follows.
Let , ; then (18) can be described as a normal equation:where and denote the transpose of and .
For our analysis, we assume a Höldertype smoothness assumption, i.e., that the minimal norm solution of the errorfree problem (1) satisfies a smoothness condition of the form, for some constant ,With .
Here is the singular system of the operator . is a parameter which controls the a priori assumptions on the regularity of the domain set.
Theorem 2 ([17, Theorem 3.1]). Let and . is a regularizing filter and assume that for ,and where and , are constants independent of . The definition of is the same as that of . Then with the a priori parameter choice The NFTR method induced by the filter is order optimal for all .
Lemma 3. The regularizing filter from (24) with fulfills Theorem 2 with and .
Proof. The filter is continuous on . The regularizing properties of the filter are easily verified. One sees thatHence, as long as , the suprema in Theorem 2 are attained as local maximal, which can be derived by simple calculus. One obtainsandConsequently, by Theorem 2, the NFTR method (15) is order optimal for and .
4. The Algorithm for Solving Unconstrained Optimization Problem
In general, it has always been known that the regular solution of (15) may give rise to an arbitrarily large perturbation in actual solution. However, one of the highlights of this work is expressed as follows: (15) is deemed considered as an unconstrained optimization problem, and the optimal solution of (15) can be obtained by using some optimization algorithms. In this paper, an improved supermemory gradient (ISMG) method was introduced to solve objective function of (15). Most of the wellknown iterative algorithms for solving (15) take the following form [46–49]:where is the search direction.
In this paper, a new search direction of ISMG method is determined as follows:where denotes the gradient of at the point , and . is defined as follows:
of (41) is the step length, which can be computed using modified nonmonotone line search technique, , and satisfies the following inequalities:where , and .
Algorithm 4. Step 1. Given an initial point , , , and ; is a given positive integer, , and , set .
Step 2. Compute ; if , then terminate; else go to the next step.
Step 3. Compute based on (42).
Step 4. Compute based on (44).
Step 5. Set .
Step 6. Set and go to Step 2.
Lemma 5. For any line search, the search direction is defined by (42); then one has
Proof. If , so we haveand , so it has , .
Hence, we obtainIf , according to (42) and (43), we can obtain the following:Then, we have Hence, (45) is proved.
Lemma 6. For any , then one has
Proof. If , so we haveIf , according to (42) and (43), we can obtain the following:So we have Hence, (50) is proved.
It is clear that we can draw the following conclusion according to (45) and (50):
5. The Convergence Properties Analysis for Optimal Algorithm
In this section, we propose an algorithm and then we study the global convergence property of this algorithm. Firstly, we make the following two assumptions, which have been widely used in the literature to analyze the global convergence of the presented method.
Assumption 7. The objective function is considered to be bounded in the following circumstances:
Assumption 8. The gradient is Lipschitz continuous; there exists a constant , for any ; we have
Lemma 9. Suppose that Assumptions 7 and 8 hold; then Algorithm 4 is wellposed; that is, there exists the step length , which makes (44) hold.
Proof. According to (44), we have It indicates that the following formula is set up for when there exists , and .Hence, (44) is proved.
Lemma 10. Suppose that Assumptions 7 and 8 hold, , and there is a constant ; then
Proof. is defined; we just need proof that . First of all, we prove it with a contradiction; suppose that holds, and there are a series of subfamily ; then Set , and is the maximum in , which meets (44). Thus, can not meet (44). Then Further, we have Based on the mean value theorem, then in which .
Thus, it follows from (62) and (63) that From Assumption 8 and Cauchy Schwartz inequality, we can obtainFrom (64) and (65), it follows thatThen, we haveCombining (67) and (54), we obtainObviously, we clearly see that (68) and (60) are contradictory. Hence, we haveThen, combining (44) and (45), we can obtainin which, set .
Hence, (59) is proved.
According to (see [50, 51]), it is clear that the sequence is contained in , and there exists a constant , such that
, suppose a<x<y<b; then it can satisfy the following conditions: , for small enough. Because is a convex function, so we obtainLet ; we haveSet , then Hence, we get
Therefore, according to the above discussion, we can draw a conclusion that the objective function defined in (15) is differentiable and Lipschitz continuous.
6. Examples and Discussions
In this section, a mathematical example and an engineering example are investigated to clarify how to solve the solution of the illposed problem quickly and accurately by using a novel fractional Tikhonov regularization (NFTR) coupled with an improved supermemory gradient (ISMG) method.
6.1. A Mathematical Example
In this section, our first example is a severely illposed Fredholm integral equation of the first kind given bySet , ; then in which the perturbation of the righthand side is set as follows:where , in which , , and denote the standard deviation of , a random number within , and the noise level, respectively.
Then (77) was discretized, which can be obtained:in which , .
The problem of solving (76) is an illposed problem; we solve the approximate solution of (76) with the righthand side given a perturbation. The true solution of (76) is ; then we need to calculate the identified solution . Here, TR method, FTR method, and the present method in this paper are applied to deal with the problem.
In order to further discuss the quality of the different methods used and assess the degree of the approximation of the identified result to the true result, we provide the following evaluation metrics.
Relative error (RE):Correlation coefficient (CC):
The program processing is executed in the MATLAB regularization tools package, and the condition number of the matrix is calculated by the MATLAB function cond; it can be found that , which means that the matrix is singular.
In our experiments, the noise vector is selected as a normal random distribution vector with zero mean and normalized to an ideal noise level:In this experiment, the regularization parameter is selected by using the discrepancy principle, in which and . Other parameters are set to the following: , , , , and The computer stops the iteration processing when is met.
denotes the optimal value of the fractional order that gives the most accurate identified result of (76) when is selected using the discrepancy principle ( and ).
In this section, we employ the standard TR, FTR, and the present method in the paper to identify the results of , respectively.
First of all, when and the noise level is set to 10%, 5%, and 1%, then RE and CC and iterative steps of the identified result are listed in Table 1.

Table 1 shows that whether the noise level is set to 10%, 5%, or 1%, RE and iterative steps of the identified result with application of the above methods are seen to increase with ; then CC is the opposite.
Moreover, Table 1 also shows that when , RE and iterative steps of the identified result with application of the above methods are seen to increase with the noise level; then CC is the opposite.
It can be seen clearly that the evaluation indicators using the present method are associated with minimum RE, minimum iterative steps, and maximum CC in comparison to the standard TR method and FTR method.
In a word, Table 1 shows that the identified result of the present method in this paper is closer to the true solution than that of the other methods.
Hence, it can be seen clearly that the evaluation indicators using the present method are associated with minimum RE, minimum iterative steps, and maximum CC in comparison to the other methods, which demonstrates that the present method in this paper is superior to the standard TR and FTR method.
6.2. An Engineering Example
In this section, the present method is applied to an engineering example of the identification problem of dynamic force generated between conical pick and coalseam structure.
6.2.1. Material Properties
A schematic of conical pick and coalseam structure is shown in Figure 1. In this diagram, the size of the coalseam structure specimen manufactured artificially was set as 2000 mm 800 mm 1750 mm. The density of conical pick is 7800 kg/m^{3} and the density of coalseam structure is 1607.98 kg/m^{3}; the elastic modulus of conical pick is 600 MPa and the elastic modulus of coalseam structure is 322.09 MPa; the Poisson ratio of conical pick is 0.25 and the Poisson ratio of coalseam structure is 0.25, respectively [52–54].
6.2.2. Experimental Procedure
In our current study, the experimental procedure was carried out on an experimental setup with rotatable and adjustable parameter which was shown in Figure 2. The experimental setup includes a test bed and a test system. The test bed mainly includes the following devices: motor, reducer, coupling, cutting mechanism, platform, and coalseam structure. The test system includes the forcemeasured device, force sensor, the signal amplifier, and the DaspV10 intelligent data acquisition and signal processing system [55].
It is clearly seen from Figure 3 that the conical pick is installed on the forcemeasured device, the various surfaces of which are intercepting with the force sensor, once the coalseam structure is hit by conical pick, the deformation of a force sensor is converted into a signal, which is called the dynamic force, then the dynamic force is converted by a signal amplifier, finally recorded by V10Dasp data intelligent acquisition and signal processing system.
6.2.3. The Selection of the Optimal Fractional Order
In our experiment, the installation angle of conical pick is 45°, the maximum cutting thickness of conical pick is 20 mm, the hauling speed is 0.8 r/min, and the rotary arm speed is 30 m/min. In order to effectively identify dynamic force generated between conical pick and coalseam structure, first we provide the measured displacement response within 0.3 seconds, which is shown in Figure 4. Since the measured displacement response can be given by the experiment, then we can use the present method in the paper to identify dynamic force.
In this part, we can study the effect of different fractional order on the results of dynamic force identification in order to determine the most optimal fractional order.
First of all, all program codes were made in MATLAB 7.0 and Windows 7 operating system. On the one hand, is obtained using (42); all parameters are set to the following: , , , , , and . The iteration processing stops when is met.
The identified results of dynamic force are achieved by using the present method in this paper, which are shown in Figures 5(a), 5(b), 5(c), 5(d), 5(e), 5(f), 5(g), 5(h), and 5(i), respectively. RE and CC and iterative steps of the identified results under different fractional order () are listed in Table 2.

(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
Figure 5(a) illustrates that the identified force is gained at , and Figure 5(b) provides the identified force at , the identified force at can be seen from Figure 5(c), Figure 5(d) gives the identified force at , the identified force at is shown in Figure 5(e), Figure 5(f) illustrates that the identified force is gained at , Figure 5(g) provides the identified force at , the identified force at can be seen from Figure 5(h), and Figure 5(i) gives the identified force at
We can see from Figures 5(a), 5(b), 5(c), 5(d), 5(e), 5(f), 5(g), 5(h), and 5(i) that the different fractional order can identify the dynamic force generated between conical pick and coalseam structure from the measured displacement response. However, the performance of dynamic force identification results has a certain difference.
Figures 5(a) and 5(b) illustrate that the identified force is not ideal owing to the smaller fractional order. On the contrary, when the fractional order is larger, the identified force is very seriously deviating from the actual force, which can be seen from Figures 5(h) and 5(i). Although Figures 5(c) and 5(d) can identify the contours of the load, there is a big error with the actual load. Also, Figures 5(f) and 5(g) can also identify the contours of the load, but not the ideal solution we want. However, we can find that Figure 5(e) is the relatively ideal result that we expect. Hence, within the scale of experiment, we know that the optimal fractional order of dynamic force identification using the present method is 0.5.
In order to confirm the optimal fractional order, RE and CC and iterative steps are listed in Table 2. It is clearly seen from Table 2 that RE and iterative steps of Figures 5(a), 5(b), 5(c), and 5(d) are larger than those of Figure 5(e). However, RE and iterative steps of Figure 5(e) are smaller than those of Figures 5(f), 5(g), 5(h), and 5(i). And the CC value is the opposite. Hence, compared with the other fractional order, we find from Table 2 that the fractional order has the smallest RE, the largest CC, and the least iterative steps.
Taking into account the above analysis, we can draw a conclusion that the optimal fractional order () using the present method makes it more successful to identify dynamic force generated between conical pick and coalseam structure based on the measured displacement response.
6.2.4. Comparison of Dynamic Force Identification Methods
The proposed method in this paper is compared with the standard TR method and FTR method, and the identified results of the three methods are shown in Figures 6(a), 6(b), and 6(c), respectively.
(a) The standard TR method
(b) FTR method
(c) Present method
Figure 6(a) explains that the identified force is gained using the standard TR method at , Figure 6(b) explains that the identified force is given by FTR method at , and the identified result at is shown in Figure 6(c) using the present method in the paper.
We can find from Figures 6(a), 6(b), and 6(c) that the three methods can all identify dynamic force; however, the present method in the paper can successfully and effectively identify the details of dynamic force in comparison to the standard TR method and FTR method.
In order to deeply distinguish the stability of the proposed method in this paper, we give the filter functions curves of the three methods with their corresponding optimal regular parameters () shown in Figure 7. In addition, the evaluation metrics of the identified results through three methods are listed in Table 3.

We can clearly find from Table 3 that the present method under the fractional order has the smallest RE, the largest CC, and the least iterative steps.
Figure 7 shows that the three curves basically coincided in the small singular value part with the regular parameters. Therefore, the three methods can be used to modify small singular values to suppress uncertain noise. However, with regard to the parts of the middle and large singular value, the three filter functions of the present method in the paper have relatively larger value in comparison to the standard TR and FTR method. In addition, Table 3 demonstrates that the identified result of the proposed method is better than that of the standard TR method and FTR method.
Taking into account the above analysis and discussion, we can conclude that the filter functions of the present method in the paper can not only retain the details of the solution components corresponding to smaller singular values, but also resist the solution components corresponding to larger singular values. In other words, the present method in the paper can reduce the illposedness of dynamic force identification and can offer a stable identified result.
7. Conclusions
This paper presents a novel inverse technique to offer an optimal solution for the illposed problems in mathematical and practical engineering. The inverse problem models are uniformly represented as a Fredholm integral equation of the first kind which has the illposedness to a certain extent. A novel fractional Tikhonov regularization (NFTR) method is proposed to perform an effective inverse identification. The smoothing functional of the illposed problem processed by the proposed method is considered as an unconstrained optimization problem; an improved supermemory gradient (ISMG) method is introduced to offer an optimal solution.
A mathematical problem and an engineering example of the identification problem of dynamic force generated between conical pick and coalseam structure show that the result of the present method is compared with that of the standard TR and FTR methods; new findings can be seen; that is, the present method has a better effect and stronger robustness, which can not only improve accuracy and stability of the inverse identification problem, but also accelerate the rate of convergence and enhance the details. Therefore, we can make a clear conclusion that the present method in this paper can reduce the illposedness of the inverse problems and provide a unified optimization solution. In addition, there is reason to believe that the present method can offer a reference for future research.
Data Availability
This section describes the data used in this research. Readers can find the source of data in the following ways: (i) https://www.hindawi.com/journals/mpe/2017/9125734/ and (ii) https://doi.org/10.4028/www.scientific.net/AMM.577.196.
Conflicts of Interest
The authors declare that they have no conflicts of interest regarding the publication of this article.
Acknowledgments
This work was supported by the Chinese National Natural Science Foundation (Contracts nos. 51674106 and 51274091).
References
 T. S. Jang, H. Baek, S. L. Hana, and T. Kinoshita, “Indirect measurement of the impulsive load to a nonlinear system from dynamic responses: inverse problem formulation,” Mechanical Systems and Signal Processing, vol. 24, no. 6, pp. 1665–1681, 2010. View at: Publisher Site  Google Scholar
 T. Hein and B. Hofmann, “On the nature of illposedness of an inverse problem arising in option pricing,” Inverse Problems, vol. 19, no. 6, pp. 1319–1338, 2003. View at: Publisher Site  Google Scholar  MathSciNet
 T. S. Jang, H. Baek, M. C. Kim, and B. Y. Moon, “A new method for detecting the timevarying nonlinear damping in nonlinear oscillation systems: nonparametric identification,” Mathematical Problems in Engineering, Art. ID 749309, 12 pages, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 S.J. Kim and S.K. Lee, “Experimental identification for inverse problem of a mechanical system with a nonminimum phase based on singular value decomposition,” Journal of Mechanical Science and Technology, vol. 22, no. 8, pp. 1504–1509, 2008. View at: Publisher Site  Google Scholar
 N. S. Hoang and A. G. Ramm, “Solving illconditioned linear algebraic systems by the dynamical systems method,” Inverse Problems in Science and Engineering, vol. 16, no. 5, pp. 617–630, 2008. View at: Publisher Site  Google Scholar  MathSciNet
 W. Luo, “Parameter identifiability of ship manoeuvring modeling using system identification,” Mathematical Problems in Engineering, vol. 2016, Article ID 8909170, 10 pages, 2016. View at: Publisher Site  Google Scholar
 A. Beck and A. BenTal, “On the solution of the Tikhonov regularization of the total least squares problem,” SIAM Journal on Optimization, vol. 17, no. 1, pp. 98–118, 2006. View at: Publisher Site  Google Scholar  MathSciNet
 H. Egger and H. W. Engl, “Tikhonov regularization applied to the inverse problem of option pricing: convergence analysis and rates,” Inverse Problems, vol. 21, no. 3, pp. 1027–1045, 2005. View at: Publisher Site  Google Scholar  MathSciNet
 S. G. Solodky and G. L. Myleiko, “About regularization of severely illposed problems by standard Tikhonov's method with the balancing principle,” Mathematical Modelling and Analysis, vol. 19, no. 2, pp. 199–215, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 S. George and P. Jidesh, “Reconstruction of signals by standard Tikhonov method,” Applied Mathematical Sciences, vol. 5, no. 5760, pp. 2819–2829, 2011. View at: Google Scholar  MathSciNet
 T. S. Jang, H. G. Sung, S. L. Han, and S. H. Kwon, “Inverse determination of the loading source of the infinite beam on elastic foundation,” Journal of Mechanical Science & Technology, vol. 22, no. 12, pp. 2350–2356, 2008. View at: Publisher Site  Google Scholar
 J. Gao, D. Wang, and J. Peng, “A Tikhonovtype regularization method for identifying the unknown source in the modified Helmholtz equation,” Mathematical Problems in Engineering, vol. 2012, Article ID 878109, 13 pages, 2012. View at: Google Scholar
 V. Melicher and V. Vrabel, “On a continuation approach in Tikhonov regularization and its application in piecewiseconstant parameter identification,” Inverse Problems, vol. 29, no. 11, pp. 193–212, 2013. View at: Google Scholar
 B. Weber, P. Paultre, and J. Proulx, “Structural damage detection using nonlinear parameter identification with Tikhonov regularization,” Structural Control and Health Monitoring, vol. 14, no. 3, pp. 406–427, 2007. View at: Publisher Site  Google Scholar
 V. A. Postnov, “Use of Tikhonov's regularization method for solving identification problem for elastic systems,” Mechanics of Solids, vol. 45, no. 1, pp. 51–56, 2010. View at: Publisher Site  Google Scholar
 T. A. Johansen, “On Tikhonov regularization, bias and variance in nonlinear system identification,” Automatica, vol. 33, no. 3, pp. 441–446, 1997. View at: Publisher Site  Google Scholar  MathSciNet
 D. Gerth, E. Klann, R. Ramlau, and L. Reichel, “On fractional Tikhonov regularization,” Journal of Inverse and ILLPosed Problems, vol. 23, no. 6, pp. 611–625, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 T. K. Huckle and M. Sedlacek, “TikhonovPhillips regularization with operator dependent seminorms,” Numerical Algorithms, vol. 60, no. 2, pp. 339–353, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 D. Bianchi, A. Buccini, M. Donatelli, and S. SerraCapizzano, “Iterated fractional Tikhonov regularization,” PAMM, vol. 15, no. 1, pp. 581582, 2015. View at: Publisher Site  Google Scholar
 D. Bianchi and M. Donatelli, “On generalized iterated Tikhonov regularization with operatordependent seminorms,” Electronic Transactions on Numerical Analysis, vol. 47, pp. 73–99, 2017. View at: Google Scholar  MathSciNet
 M. E. Hochstenbach and L. Reichel, “Fractional Tikhonov regularization for linear discrete illposed problems,” BIT Numerical Mathematics, vol. 51, no. 1, pp. 197–215, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 R. H. Chan, A. Lanza, S. Morigi, and F. Sgallari, “An adaptive strategy for the restoration of textured images using fractional order regularization,” Numerical Mathematics: Theory, Methods and Applications, vol. 6, no. 1, pp. 276–296, 2013. View at: Google Scholar  MathSciNet
 M. D. RuizMedina, J. M. Angulo, and V. V. Anh, “Fractionalorder regularization and wavelet approximation to the inverse estimation problem for random fields,” Journal of Multivariate Analysis, vol. 85, no. 1, pp. 192–216, 2003. View at: Publisher Site  Google Scholar  MathSciNet
 J. Zhang, Z. Wei, and L. Xiao, “A fast adaptive reweighted residualfeedback iterative algorithm for fractionalorder total variation regularized multiplicative noise removal of partlytextured images,” Signal Processing, vol. 98, pp. 381–395, 2014. View at: Publisher Site  Google Scholar
 A. Y. AlBayati and R. S. Muhammad, “New scaled sufficient descent conjugate gradient algorithm for solving unconstraint optimization problems,” Journal of Computer Science, vol. 6, no. 5, pp. 511–518, 2010. View at: Publisher Site  Google Scholar
 W. W. Hager and H. Zhang, “The limited memory conjugate gradient method,” SIAM Journal on Optimization, vol. 23, no. 4, pp. 2150–2168, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 Y. J. Wang, C. Y. Wang, and N. H. Xiu, “A family of supermemory gradient projection methods for constrained optimization,” Optimization. A Journal of Mathematical Programming and Operations Research, vol. 51, no. 6, pp. 889–905, 2002. View at: Publisher Site  Google Scholar  MathSciNet
 K. W. Ng and A. Rohanin, “Solving optimal control problem of monodomain model using hybrid conjugate gradient methods,” Mathematical Problems in Engineering, vol. 2012, Article ID 734070, 14 pages, 2012. View at: Google Scholar
 Y. Narushima and H. Yabe, “Global convergence of a memory gradient method for unconstrained optimization,” Computational optimization and applications, vol. 35, no. 3, pp. 325–346, 2006. View at: Publisher Site  Google Scholar  MathSciNet
 J.B. Jian, Y.F. Zeng, and C.M. Tang, “A generalized supermemory gradient projection method of strongly subfeasible directions with strong convergence for nonlinear inequality constrained optimization,” Computers & Mathematics with Applications, vol. 54, no. 4, pp. 507–524, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Jia, Z. Yang, N. Guo, and L. Wang, “Random dynamic load identification based on error analysis and weighted total least squares method,” Journal of Sound and Vibration, vol. 358, pp. 111–123, 2015. View at: Publisher Site  Google Scholar
 J. Liu, X. Sun, X. Han, C. Jiang, and D. Yu, “Dynamic load identification for stochastic structures based on Gegenbauer polynomial approximation and regularization method,” Mechanical Systems and Signal Processing, vol. 56, pp. 35–54, 2015. View at: Publisher Site  Google Scholar
 T. Aktosun and C. van der Mee, “Solution of the inverse scattering problem for the threedimensional Schroedinger equation using a Fredholm integral equation,” SIAM Journal on Mathematical Analysis, vol. 22, no. 3, pp. 717–731, 2006. View at: Google Scholar
 J. N. Franklin, “Minimum principles for illposed problems,” SIAM Journal on Mathematical Analysis, vol. 9, no. 4, pp. 638–650, 1978. View at: Publisher Site  Google Scholar  MathSciNet
 N. N. Abdelmalek, “An algorithm for the solution of illposed linear systems arising from the discretization of Fredholm integral equation of the first kind,” Journal of Mathematical Analysis and Applications, vol. 97, no. 1, pp. 95–111, 1983. View at: Publisher Site  Google Scholar  MathSciNet
 G. L. Mazzieri and R. D. Spies, “Regularization methods for illposed problems in multiple Hilbert scales,” Inverse Problems, vol. 28, no. 5, 055005, 30 pages, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 V. A. Morozov, “Application of the regularization method to the solution of an illposed problem,” Proceedings of the Royal Society of Medicine, vol. 18, no. 4, pp. 401–406, 1965. View at: Google Scholar
 N. H. Tuan and D. D. Trong, “A simple regularization method for the illposed evolution equation,” Czechoslovak Mathematical Journal, vol. 61(136), no. 1, pp. 85–95, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 F. Stout and J. H. Kalivas, “Tikhonov regularization in standardized and general form for multivariate calibration with application towards removing unwanted spectral artifacts,” Journal of Chemometrics, vol. 20, no. 12, pp. 22–33, 2006. View at: Publisher Site  Google Scholar
 Y. Wei, P. Xie, and L. Zhang, “Tikhonov Regularization and Randomized GSVD,” Siam Journal on Matrix Analysis and Applications, vol. 37, no. 2, pp. 649–675, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 S. C. Omowunmi and A. A. Susu, “Application of Tikhonov Regularization Technique to the Kinetic Data of an Autocatalytic Reaction: Pyrolysis of NEicosane,” Engineering Journal, vol. 03, no. 12, pp. 1161–1170, 2011. View at: Publisher Site  Google Scholar
 M. Z. Nashed, “The theory of Tikhonov regularization for fredholm equations of the first kind (C. W. Groetsch),” Siam Review, vol. 28, no. 1, pp. 116–118, 2006. View at: Google Scholar
 H. Liu, X. Wang, and C. Lu, “Rolling bearing fault diagnosis under variable conditions using HilbertHuang transform and singular value decomposition,” Mathematical Problems in Engineering, vol. 2014, Article ID 765621, 10 pages, 2014. View at: Publisher Site  Google Scholar
 G. M. Vainikko, “The discrepancy principle for a class of regularization methods,” USSR Computational Mathematics and Mathematical Physics, vol. 22, no. 3, pp. 1–19, 1982. View at: Publisher Site  Google Scholar
 V. Albani, A. De Cezaro, and J. P. Zubelli, “On the choice of the Tikhonov regularization parameter and the discretization level: a discrepancybased strategy,” Inverse Problems and Imaging, vol. 10, no. 1, pp. 1–25, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 C. S. Liu, “Novel algorithms based on the conjugate gradient method for inverting illconditioned matrices, and a new regularization method to solve illposed linear systems,” Computer Modeling in Engineering and Sciences, vol. 60, no. 3, pp. 279–308, 2010. View at: Google Scholar  MathSciNet
 C.S. Liu, “An optimally generalized steepestdescent algorithm for solving illposed linear systems,” Journal of Applied Mathematics, vol. 2013, Article ID 154358, 15 pages, 2013. View at: Google Scholar
 C.S. Liu, “Modifications of steepest descent method and conjugate gradient method against noise for illposed linear systems,” Communications in Numerical Analysis, vol. 2012, Article ID cna00115, 24 pages, 2012. View at: Google Scholar
 G. Zoutendijk, “Nonlinear programming, computational methods,” in Integer and Nonlinear Programming, pp. 37–86, NorthHolland, Amsterdam, The Netherlands, 1970. View at: Google Scholar  MathSciNet
 P. Wolfe, “Convergence conditions for ascent methods,” SIAM Review, vol. 11, pp. 226–235, 1969. View at: Publisher Site  Google Scholar  MathSciNet
 P. Wolfe, “Convergence Conditions for Ascent Methods II: Some Corrections,” Siam Review, vol. 13, no. 2, pp. 226–235, 2006. View at: Google Scholar
 M. P. Baldwin and T. J. Dunkerton, “Stratospheric harbingers of anomalous weather regimes,” Science, vol. 294, no. 5542, pp. 581–584, 2001. View at: Publisher Site  Google Scholar
 C.S. Liu, C.P. Ren, and D.G. Li, “Reconstruction and deduction of cutting coal and rock load spectrum on modified discrete regularization algorithm,” Journal of China Coal Society, vol. 39, no. 5, pp. 981–986, 2014. View at: Publisher Site  Google Scholar
 C. S. Liu, C. P. Ren, and F. Han, “Study on timefrequency spectrum characteristic of dynamic cutting load based on wavelet regularization,” Applied Mechanics and Materials, vol. 577, pp. 196–200, 2014. View at: Publisher Site  Google Scholar
 C. Ren, N. Wang, and C. Liu, “Identification of random dynamic force using an improved maximum entropy regularization combined with a novel conjugate gradient,” Mathematical Problems in Engineering, vol. 2017, Article ID 9125734, 14 pages, 2017. View at: Google Scholar
Copyright
Copyright © 2018 Nengjian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.