- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Abstract and Applied Analysis
Volume 2013 (2013), Article ID 645262, 9 pages
Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect
1College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
2Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
Received 3 September 2012; Accepted 4 January 2013
Academic Editor: Tingwen Huang
Copyright © 2013 Guowei Yang 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.
This paper considers dynamical behaviors of a class of fuzzy impulsive reaction-diffusion delayed cellular neural networks (FIRDDCNNs) with time-varying periodic self-inhibitions, interconnection weights, and inputs. By using delay differential inequality, -matrix theory, and analytic methods, some new sufficient conditions ensuring global exponential stability of the periodic FIRDDCNN model with Neumann boundary conditions are established, and the exponential convergence rate index is estimated. The differentiability of the time-varying delays is not needed. An example is presented to demonstrate the efficiency and effectiveness of the obtained results.
The fuzzy cellular neural networks (FCNNs) model, which combines fuzzy logic with the structure of traditional neural networks (CNNs) [1–3], has been proposed by Yang et al. [4, 5]. Unlike previous CNNs structures, the FCNNs model has fuzzy logic between its template and input and/or output besides the “sum of product” operation. Studies have shown that the FCNNs model is a very useful paradigm for image processing and pattern recognition [6–8]. These applications heavily depend on not only the dynamical analysis of equilibrium points but also on that of the periodic oscillatory solutions. In fact, the human brain is naturally in periodic oscillatory , and the dynamical analysis of periodic oscillatory solutions is very important in learning theory [10, 11], because learning usually requires repetition. Moreover, an equilibrium point can be viewed as a special periodic solution of neural networks with arbitrary period. Stability analysis problems for FCNNs with and without delays have recently been probed; see [12–22] and the references therein. Yuan et al.  have investigated stability of FCNNs by linear matrix inequality approach, and several criteria have been provided for checking the periodic solutions for FCNNs with time-varying delays. Huang  has probed exponential stability of fuzzy cellular neural networks with distributed delay, without considering reaction-diffusion effects.
Strictly speaking, reaction-diffusion effects cannot be neglected in both biological and man-made neural networks [19–32], especially when electrons are moving in noneven electromagnetic field. In , stability is considered for FCNNs with diffusion terms and time-varying delay. Wang and Lu  have probed global exponential stability of FCNNs with delays and reaction-diffusion terms. Song and Wang  have studied dynamical behaviors of fuzzy reaction-diffusion periodic cellular neural networks with variable coefficients and delays without considering pulsing effects. Wang et al.  have discussed exponential stability of impulsive stochastic fuzzy reaction-diffusion Cohen-Grossberg neural networks with mixed delays. Zhao and Mao  have investigated boundedness and stability of nonautonomous cellular neural networks with reaction-diffusion terms. Zhao and Wang  have considered existence of periodic oscillatory solution of reaction-diffusion neural networks with delays without fuzzy logic and impulsive effect.
As we all know, many practical systems in physics, biology, engineering, and information science undergo abrupt changes at certain moments of time because of impulsive inputs . Impulsive differential equations and impulsive neural networks have been received much interest in recent years; see, for example, [34–42] and the references therein. Yang and Xu  have investigated existence and exponential stability of periodic solution for impulsive delay differential equations and applications. Li and Lu  have discussed global exponential stability and existence of periodic solution of Hopfield-type neural networks with impulses without reaction-diffusion. To the best of our knowledge, few authors have probed the existence and exponential stability of the periodic solutions for the FIRDDCNN model with variable coefficients, and time-varying delays. As a result of the simultaneous presence of fuzziness, pulsing effects, reaction-diffusion phenomena, periodicity, variable coefficients and delays, the dynamical behaviors of this kind of model become much more complex and have not been properly addressed, which still remain important and challenging.
Motivated by the above discussion, we will establish some sufficient conditions for the existence and exponential stability of periodic solutions of this kind of FIRDDCNN model, applying delay differential inequality, -matrix theory, and analytic methods. An example is employed to demonstrate the usefulness of the obtained results.
Notations. Throughout this paper, and denote, respectively, the -dimensional Euclidean space and the set of all real matrices. The superscript “” denotes matrix transposition and the notation (resp., ), where and are symmetric matrices, means that is positive semidefinite (resp., positive definite). is a bounded compact set in space with smooth boundary and measure ; Neumann boundary condition is the outer normal to ; is the space of real functions which are for the Lebesgue measure. It is a Banach space with the norm , where ,, , . For function with positive period , we denote . Sometimes, the arguments of a function or a matrix will be omitted in the analysis when no confusion can arise.
Consider the impulsive fuzzy reaction-diffusion delayed cellular neural networks (FIRDDCNN) model: where is the number of neurons in the network and corresponds to the state of the th neuron at time and in space ; is the diffusion-matrix and ; is the Laplace operator; denotes the activation function of the th unit and the activation function of the th unit; is an input at time ; represents the rate with which the th unit will reset its potential to the resting state in isolation when disconnected from the networks and external inputs at time ; and are elements of feedback template and feed forward template at time , respectively. Moreover, in model (1), , and are elements of fuzzy feedback MIN template, fuzzy feedback MAX template, fuzzy feed forward MIN template, and fuzzy feed forward MAX template at time , respectively; the symbols “" and “" denote the fuzzy AND and fuzzy OR operation, respectively; time-varying delay is the transmission delay along the axon of the th unit and satisfies ( is a constant); the initial condition is bounded and continuous on , where . The fixed moments satisfy . and denote the right-hand and left-hand limits at , respectively. We always assume , for all . The initial value functions belong to . for every ; for any fixed is continuous for all but at most countable points and at these points, and exist, , where and denote the right-hand and left-hand limit of the function , respectively. Especially, let . For any , suppose that exists as a finite number and introduce the norm , where .
Throughout the paper, we make the following assumptions. There exists a positive diagonal matrix , and such that for all ., and are periodic function with a common positive period for all . For , there exists such that , and are Lipschitz continuous in .
Definition 1. The model in (1) is said to be globally exponentially periodic if (i) there exists one -periodic solution and (ii) all other solutions of the model converge exponentially to it as .
Definition 2 (see ). Let , where and . Then the function is called an -function, if (i) for every , there holds , for , where and ; (ii) every th element of satisfies for any , where arbitrary and belong to and have the same th component . Here, .
Definition 3 (see ). A real matrix is said to be a nonsingular -matrix if ; and all successive principal minors of are positive.
Lemma 5 (see ). Assume that is an -function, and (i) , , (ii) , , , where , . Then , .
3. Main Results and Proofs
We should first point out that, under assumptions (H1), (H2), and (H3), the FIRDDCNN model (1) has at least one -periodic solution of . The proof of the existence of the -periodic solution of (1) can be carried out similar to [26, 28] by the nonlinear functional analysis methods such as topological degree and here is omitted. We will mainly discuss the uniqueness of the periodic solution and its exponential stability.
Theorem 6. Assume that – holds. Furthermore, assume that the following conditions hold is a nonsingular -matrix. The impulsive operators is Lipschitz continuous in ; that is, there exists a nonnegative diagnose matrix such that for all , , where , ,., where .
Then the model (1) is global exponential periodic and the exponential convergence rate index and can be estimated by where and , , , , satisfies .
Proof. For any , let be a periodic solution of the system (1) starting from and , a solution of the system (1) starting from . Define
and we can see that for all . Let , then from (1) we get
for all , , .
Multiplying both sides of (6) by and integrating it in , we have for , , . By boundary condition and Green Formula, we can get Then, from (8), (9), (H1)-(H2), Lemma 4, and the Holder inequality, Thus, for . Since is a nonsingular -matrix, there exists a vector such that Considering functions we know from (11) that and is continuous. Since , is strictly monotonically increasing, there exists a scalar such that Choosing , we have That is, Furthermore, choose a positive scalar large enough such that For any , let From (15)–(17), we obtain where and . It is easy to verify that is an -function. It follows also from (16) and (17) that Denote where , then From (10), we can obtain Now, it follows from (18)–(22) and Lemma 5 that Letting , we have And moreover, from (24), we get Let , then . Define ; it follows from (25) and the definitions of and that It is easily observed that Because (26) holds, we can suppose that for inequality holds, where . When , we note (H5) that where is the spectral radius of . Let , by (28), (29), and , we obtain Combining (10), (17), (30), and Lemma 5, we get Applying mathematical induction, we conclude that From (H6) and (32), we have This means that choosing a positive integer such that Define a Poincare mapping by Then Setting in (34), from (35) and (37), we have which implies that is a contraction mapping. Thus, there exists a unique fixed point such that From (37), we know that is also a fixed point of , and then it follows from the uniqueness of the fixed point that Let be a solution of the model (1), then is also a solution of the model (1). Obviously, for all . Hence, , which shows that is exactly one -periodic solution of model (1). It is easy to see that all other solutions of model (1) converge to this periodic solution exponentially as , and the exponential convergence rate index is . The proof is completed.
Remark 7. When , , , , , , , , , and , and are constants), then the model (1) is changed into For any positive constant , we have , , , , , , , , , and for . Thus, the sufficient conditions in Theorem 6 are satisfied.
Remark 8. If , the model (1) is changed into which has been discussed in . As Song and Wang have pointed out, the model (43) is more general than some well-studied fuzzy neural networks. For example, when , and are all constants, the model in (43) reduces the model which has been studied by Huang . Moreover, if , , , , then model (42) covers the model studied by Yang et al. [4, 5] as a special case. If and is assumed to be differentiable for , then model (43) can be specialized to the model investigated in Liu and Tang  and Yuan et al. . Obviously, our results are less conservative than that of the above-mentioned literature, because they do not consider impulsive effects.
4. Numerical Examples
Example 9. Consider a two-neuron FIRDDCNN model: where , , , , , , , , , , , , , , , , , , , , , , , , , , , , . We assume that there exists such that . Obviously, , and satisfy the assumption (H1) with and (H2) and (H3) are satisfied with a common positive period is a nonsingular -matrix. The conditions of Theorem 6 are satisfied, hence there exists exactly one -periodic solution of the model and all other solutions of the model converge exponentially to it as . Furthermore, the exponential converging index can be calculated as , because here and . The simulation results are shown in Figures 1, 2, 3, and 4, respectively.
In this paper, periodicity and global exponential stability of a class of FIRDDCNN model with variable both coefficients and delays have been investigated. By using Halanay’s delay differential inequality, -matrix theory, and analytic methods, some new sufficient conditions have been established to guarantee the existence, uniqueness, and global exponential stability of the periodic solution. Moreover, the exponential convergence rate index can be estimated. An example and its simulation have been given to show the effectiveness of the obtained results. In particular, the differentiability of the time-varying delays has been removed. The dynamic behaviors of fuzzy neural networks with the property of exponential periodicity are of great importance in many areas such as learning systems.
The authors would like to thank the editors and the anonymous reviewers for their valuable comments and constructive suggestions. This research is supported by the Natural Science Foundation of Guangxi Autonomous Region (no. 2012GXNSFBA053003), the National Natural Science Foundations of China (60973048, 61272077, 60974025, 60673101, and 60939003), National 863 Plan Project (2008 AA04Z401, 2009 AA043404), the Natural Science Foundation of Shandong Province (no. Y2007G30), the Scientific and Technological Project of Shandong Province (no. 2007GG3WZ04016), the Science Foundation of Harbin Institute of Technology (Weihai) (HIT (WH) 200807), the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT. NSRIF. 2001120), the China Postdoctoral Science Foundation (2010048 1000), and the Shandong Provincial Key Laboratory of Industrial Control Technique (Qingdao University).
- L. O. Chua and L. Yang, “Cellular neural networks: theory,” IEEE Transactions on Circuits and Systems, vol. 35, no. 10, pp. 1257–1272, 1988.
- L. O. Chua and L. Yang, “Cellular neural networks: applications,” IEEE Transactions on Circuits and Systems, vol. 35, no. 10, pp. 1273–1290, 1988.
- T. Roska and L. O. Chua, “Cellular neural networks with non-linear and delay-type template elements and non-uniform grids,” International Journal of Circuit Theory and Applications, vol. 20, no. 5, pp. 469–481, 1992.
- T. Yang, L. B. Yang, C. W. Wu, and L. O. Chua, “Fuzzy cellular neural networks: theory,” in Proceedings of the 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications (CNNA '96), pp. 181–186, June 1996.
- T. Yang and L.-B. Yang, “The global stability of fuzzy cellular neural network,” IEEE Transactions on Circuits and Systems I, vol. 43, no. 10, pp. 880–883, 1996.
- T. Yang, L. B. Yang, C. W. Wu, and L. O. Chua, “Fuzzy cellular neural networks: applications,” in Proceedings of the 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications (CNNA '96), pp. 225–230, June 1996.
- S. T. Wang, K. F. L. Chung, and F. Duan, “Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection,” Neurocomputing, vol. 70, no. 7–9, pp. 1348–1359, 2007.
- T. G. Barbounis and J. B. Theocharis, “A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation,” Neurocomputing, vol. 70, no. 7–9, pp. 1525–1542, 2007.
- H. Zhao, “Global exponential stability and periodicity of cellular neural networks with variable delays,” Physics Letters A, vol. 336, no. 4-5, pp. 331–341, 2005.
- S. Townley, A. Ilchmann, M. G. Weiss et al., “Existence and learning of oscillations in recurrent neural networks,” IEEE Transactions on Neural Networks, vol. 11, no. 1, pp. 205–214, 2000.
- Z. Huang and Y. Xia, “Exponential periodic attractor of impulsive BAM networks with finite distributed delays,” Chaos, Solitons & Fractals, vol. 39, no. 1, pp. 373–384, 2009.
- Y. Liu and W. Tang, “Exponential stability of fuzzy cellular neural networks with constant and time-varying delays,” Physics Letters A, vol. 323, no. 3-4, pp. 224–233, 2004.
- K. Yuan, J. Cao, and J. Deng, “Exponential stability and periodic solutions of fuzzy cellular neural networks with time-varying delays,” Neurocomputing, vol. 69, no. 13–15, pp. 1619–1627, 2006.
- T. Huang, “Exponential stability of fuzzy cellular neural networks with distributed delay,” Physics Letters A, vol. 351, no. 1-2, pp. 48–52, 2006.
- G. Bao, S. Wen, and Z. Zeng, “Robust stability analysis of interval fuzzy Cohen-Grossberg neural networks with piecewise constant argument of generalized type,” Neural Networks, vol. 33, pp. 32–41, 2012.
- H. Zhao, N. Ding, and L. Chen, “Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays,” Chaos, Solitons & Fractals, vol. 40, no. 4, pp. 1653–1659, 2009.
- W. Ding, M. Han, and M. Li, “Exponential lag synchronization of delayed fuzzy cellular neural networks with impulses,” Physics Letters A, vol. 373, no. 8-9, pp. 832–837, 2009.
- L. Chen and H. Zhao, “Stability analysis of stochastic fuzzy cellular neural networks with delays,” Neurocomputing, vol. 72, no. 1–3, pp. 436–444, 2008.
- T. Huang, “Exponential stability of delayed fuzzy cellular neural networks with diffusion,” Chaos, Solitons & Fractals, vol. 31, no. 3, pp. 658–664, 2007.
- J. Wang and J. G. Lu, “Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms,” Chaos, Solitons & Fractals, vol. 38, no. 3, pp. 878–885, 2008.
- Q. Song and Z. Wang, “Dynamical behaviors of fuzzy reaction-diffusion periodic cellular neural networks with variable coefficients and delays,” Applied Mathematical Modelling, vol. 33, no. 9, pp. 3533–3545, 2009.
- C. Wang, Y. kao, and G. Yang, “Exponential stability of impulsive stochastic fuzzy reaction-diffusion Cohen-Grossberg neural networks with mixed delays,” Neurocomputing, vol. 89, pp. 55–63, 2012.
- T. Serrano-Gotarredona and B. Linares-Barranco, “Log-Domain Implementation of Complex Dynamics Reaction-Diffusion Neural Networks,” IEEE Transactions on Neural Networks, vol. 14, no. 5, pp. 1337–1355, 2003.
- J. Liang and J. Cao, “Global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays,” Physics Letters A, vol. 314, no. 5-6, pp. 434–442, 2003.
- L. Wang and D. Xu, “Asymptotic behavior of a class of reaction-diffusion equations with delays,” Journal of Mathematical Analysis and Applications, vol. 281, no. 2, pp. 439–453, 2003.
- L. Wang and D. Xu, “Global exponential stability of Hopfield reaction-diffusion neural networks with time-varying delays,” Science in China. Series F, vol. 46, no. 6, pp. 466–474, 2003.
- Z. Yang and D. Xu, “Global dynamics for non-autonomous reaction-diffusion neural networks with time-varying delays,” Theoretical Computer Science, vol. 403, no. 1, pp. 3–10, 2008.
- Y. G. Kao, C. C. Gao, and D. K. Wang, “Global exponential stability of reaction-diffusion Hopfield neural networks with continuously distributed delays,” Mathematica Applicata, vol. 21, no. 3, pp. 457–462, 2008 (Chinese).
- Y.-G. Kao, J.-F. Guo, C.-H. Wang, and X.-Q. Sun, “Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen-Grossberg neural networks with mixed delays,” Journal of the Franklin Institute, vol. 349, no. 6, pp. 1972–1988, 2012.
- H. Zhao and Z. Mao, “Boundedness and stability of nonautonomous cellular neural networks with reaction-diffusion terms,” Mathematics and Computers in Simulation, vol. 79, no. 5, pp. 1603–1617, 2009.
- H. Zhao and G. Wang, “Existence of periodic oscillatory solution of reaction-diffusion neural networks with delays,” Physics Letters A, vol. 343, no. 5, pp. 372–383, 2005.
- Q. Gan, “Exponential synchronization of stochastic Cohen-Grossberg neural networks with mixed time-varying delays and reaction-diffusion via periodically intermittent control,” Neural Networks, vol. 31, pp. 12–21, 2012.
- V. Lakshmikantham, D. D. Baĭnov, and P. S. Simeonov, Theory of Impulsive Differential Equations, vol. 6 of Series in Modern Applied Mathematics, World Scientific, Teaneck, NJ, USA, 1989.
- D. D. Bainov and P. S. Simeonov, Impulsive Differential Equations: Periodic Solutions and Applications, Longman, Harlow, UK, 1993.
- X. Song, X. Xin, and W. Huang, “Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions,” Neural Networks, vol. 29-30, pp. 80–90, 2012.
- Z. Yang and D. Xu, “Existence and exponential stability of periodic solution for impulsive delay differential equations and applications,” Nonlinear Analysis: Theory, Methods & Applications, vol. 64, no. 1, pp. 130–145, 2006.
- X. Yang, X. Liao, D. J. Evans, and Y. Tang, “Existence and stability of periodic solution in impulsive Hopfield neural networks with finite distributed delays,” Physics Letters A, vol. 343, no. 1–3, pp. 108–116, 2005.
- Y. Li and L. Lu, “Global exponential stability and existence of periodic solution of Hopfield-type neural networks with impulses,” Physics Letters A, vol. 333, no. 1-2, pp. 62–71, 2004.
- Y. Li, L. Zhu, and P. Liu, “Existence and stability of periodic solutions of delayed cellular neural networks,” Nonlinear Analysis: Real World Applications, vol. 7, no. 2, pp. 225–234, 2006.
- Z. H. Guan and G. Chen, “On delayed impulsive Hopfield neural networks,” Neural Networks, vol. 12, no. 2, pp. 273–280, 1999.
- Z. H. Guan, J. Lam, and G. Chen, “On impulsive autoassociative neural networks,” Neural Networks, vol. 13, no. 1, pp. 63–69, 2000.
- W. Ding, “Synchronization of delayed fuzzy cellular neural networks with impulsive effects,” Communications in Nonlinear Science and Numerical Simulation, vol. 14, no. 11, pp. 3945–3952, 2009.