Advances in Fuzzy Systems The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. An Efficient Ranking Technique for Intuitionistic Fuzzy Numbers with Its Application in Chance Constrained Bilevel Programming Thu, 28 Apr 2016 11:40:35 +0000 The aim of this paper is to develop a new ranking technique for intuitionistic fuzzy numbers using the method of defuzzification based on probability density function of the corresponding membership function, as well as the complement of nonmembership function. Using the proposed ranking technique a methodology for solving linear bilevel fuzzy stochastic programming problem involving normal intuitionistic fuzzy numbers is developed. In the solution process each objective is solved independently to set the individual goal value of the objectives of the decision makers and thereby constructing fuzzy membership goal of the objectives of each decision maker. Finally, a fuzzy goal programming approach is considered to achieve the highest membership degree to the extent possible of each of the membership goals of the decision makers in the decision making context. Illustrative numerical examples are provided to demonstrate the applicability of the proposed methodology and the achieved results are compared with existing techniques. Animesh Biswas and Arnab Kumar De Copyright © 2016 Animesh Biswas and Arnab Kumar De. All rights reserved. The Lattice Structure of L-Contact Relations Mon, 18 Apr 2016 12:11:30 +0000 From the point of view of graded truth approach, we define the notion of a contact relation on the collection of all -sets, discuss the connection to the set of all close, reflexive, and symmetric relations on all -ultrafilters on , and investigate the algebraic structure of all -contact relations. Xueyou Chen Copyright © 2016 Xueyou Chen. All rights reserved. Designing of Vague Logic Based 2-Layered Framework for CPU Scheduler Wed, 13 Apr 2016 09:28:29 +0000 Fuzzy based CPU scheduler has become of great interest by operating system because of its ability to handle imprecise information associated with task. This paper introduces an extension to the fuzzy based round robin scheduler to a Vague Logic Based Round Robin (VBRR) scheduler. VBRR scheduler works on 2-layered framework. At the first layer, scheduler has a vague inference system which has the ability to handle the impreciseness of task using vague logic. At the second layer, Vague Logic Based Round Robin (VBRR) scheduling algorithm works to schedule the tasks. VBRR scheduler has the learning capability based on which scheduler adapts intelligently an optimum length for time quantum. An optimum time quantum reduces the overhead on scheduler by reducing the unnecessary context switches which lead to improve the overall performance of system. The work is simulated using MATLAB and compared with the conventional round robin scheduler and the other two fuzzy based approaches to CPU scheduler. Given simulation analysis and results prove the effectiveness and efficiency of VBRR scheduler. Supriya Raheja Copyright © 2016 Supriya Raheja. All rights reserved. Power Frequency Oscillation Suppression Using Two-Stage Optimized Fuzzy Logic Controller for Multigeneration System Tue, 12 Apr 2016 11:04:28 +0000 This paper attempts to develop a linearized model of automatic generation control (AGC) for an interconnected two-area reheat type thermal power system in deregulated environment. A comparison between genetic algorithm optimized PID controller (GA-PID), particle swarm optimized PID controller (PSO-PID), and proposed two-stage based PSO optimized fuzzy logic controller (TSO-FLC) is presented. The proposed fuzzy based controller is optimized at two stages: one is rule base optimization and other is scaling factor and gain factor optimization. This shows the best dynamic response following a step load change with different cases of bilateral contracts in deregulated environment. In addition, performance of proposed TSO-FLC is also examined for changes in system parameters with different type of contractual demands between control areas and compared with GA-PID and PSO-PID. MATLAB/Simulink® is used for all simulations. Y. K. Bhateshvar and H. D. Mathur Copyright © 2016 Y. K. Bhateshvar and H. D. Mathur. All rights reserved. Predicting Geotechnical Investigation Using the Knowledge Based System Tue, 05 Apr 2016 13:02:35 +0000 The purpose of this paper is to evaluate the optimal number of investigation points and each field test and laboratory test for a proper description of a building site. These optimal numbers are defined based on their minimum and maximum number and with the equivalent investigation ratio. The total increments of minimum and maximum number of investigation points for different building site conditions were determined. To facilitate the decision-making process for a number of investigation points, an Adaptive Network Fuzzy Inference System (ANFIS) was proposed. The obtained fuzzy inference system considers the influence of several entry parameters and computes the equivalent investigation ratio. The developed model (ANFIS-SI) can be applied to characterize any building site. The ANFIS-SI model takes into account project factors which are evaluated with a rating from 1 to 10. The model ANFIS-SI, with integrated recommendations can be used as a systematic decision support tool for engineers to evaluate the number of investigation points, field tests, and laboratory tests for a proper description of a building site. The determination of the optimal number of investigative points and the optimal number of each field test and laboratory test is presented on reference case. Bojan Žlender and Primož Jelušič Copyright © 2016 Bojan Žlender and Primož Jelušič. All rights reserved. Fuzzy Human Reliability Analysis: Applications and Contributions Review Mon, 04 Apr 2016 13:34:29 +0000 The applications and contributions of fuzzy set theory to human reliability analysis (HRA) are reassessed. The main contribution of fuzzy mathematics relies on its ability to represent vague information. Many HRA authors have made contributions developing new models, introducing fuzzy quantification methodologies. Conversely, others have drawn on fuzzy techniques or methodologies for quantifying already existing models. Fuzzy contributions improve HRA in five main aspects: (1) uncertainty treatment, (2) expert judgment data treatment, (3) fuzzy fault trees, (4) performance shaping factors, and (5) human behaviour model. Finally, recent fuzzy applications and new trends in fuzzy HRA are herein discussed. P. A. Baziuk, S. S. Rivera, and J. Núñez Mc Leod Copyright © 2016 P. A. Baziuk et al. All rights reserved. A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters Mon, 28 Mar 2016 06:35:14 +0000 Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy. Boominathan Perumal and Aramudhan Murugaiyan Copyright © 2016 Boominathan Perumal and Aramudhan Murugaiyan. All rights reserved. FCM Clustering Algorithms for Segmentation of Brain MR Images Tue, 15 Mar 2016 12:38:55 +0000 The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy -means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed. Yogita K. Dubey and Milind M. Mushrif Copyright © 2016 Yogita K. Dubey and Milind M. Mushrif. All rights reserved. Type-2 Fuzzy Logic Controller of a Doubly Fed Induction Machine Tue, 15 Mar 2016 08:36:52 +0000 Interval type-2 fuzzy logic controller (IT2FLC) method for controlling the speed with a direct stator flux orientation control of doubly fed induction motor (DFIM) is proposed. The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases it is proved that their robustness and performance are less sensitive to parameters variation over conventional controllers. The synthesis of stabilizing control laws design based on IT2FLC is developed. A comparative analysis between type-1 fuzzy logic controller (T1FLC) and IT2FLC of the DFIM is shown. Simulation results show the feasibility and the effectiveness of the suggested method to the control of the DFIM under different operating conditions such as load torque and in the presence of parameters variation. Keltoum Loukal and Leila Benalia Copyright © 2016 Keltoum Loukal and Leila Benalia. All rights reserved. Two-Stage Stratified Randomized Response Model with Fuzzy Numbers Thu, 10 Mar 2016 08:40:05 +0000 We consider an allocation problem in two-stage stratified Warner’s randomized response model and minimize the variance subject to cost constraint. The costs (measurement costs and total budget of the survey) in the cost constraint are assumed as fuzzy numbers, in particular triangular and trapezoidal fuzzy numbers due to the ease of use. The problem formulated is solved by using Lagrange multipliers technique and the optimum allocation obtained in the form of fuzzy numbers is converted into crisp form using -cut method at a prescribed value of . An illustrative numerical example is presented to demonstrate the proposed problem. Mohammad Faisal Khan, Neha Gupta, and Irfan Ali Copyright © 2016 Mohammad Faisal Khan et al. All rights reserved. An ELECTRE Approach for Multicriteria Interval-Valued Intuitionistic Trapezoidal Fuzzy Group Decision Making Problems Wed, 09 Mar 2016 13:48:56 +0000 The Multiple Criteria Decision Making (MCDM) is acknowledged as the most useful branch of decision making. It provides an effective framework for comparison based on the evaluation of multiple conflicting criteria. In this paper, a method is proposed to work out multiple attribute group decision making (MAGDM) problems with interval-valued intuitionistic trapezoidal fuzzy numbers (IVITFNs) using Elimination and Choice Translation Reality (ELECTRE) method. A new ranking function based on value and ambiguity is introduced to compare the IVITFNs, which overcomes the limitations of existing methods. An illustrative numerical example is solved to verify the efficiency of the proposed method to select the better alternative. Sireesha Veeramachaneni and Himabindu Kandikonda Copyright © 2016 Sireesha Veeramachaneni and Himabindu Kandikonda. All rights reserved. A New Approach for Solving Fully Fuzzy Linear Programming by Using the Lexicography Method Wed, 02 Mar 2016 12:51:55 +0000 The fully fuzzy linear programming (FFLP) problem has many different applications in sciences and engineering, and various methods have been proposed for solving this problem. Recently, some scholars presented two new methods to solve FFLP. In this paper, by considering the fuzzy numbers and the lexicography method in conjunction with crisp linear programming, we design a new model for solving FFLP. The proposed scheme presented promising results from the aspects of performance and computing efficiency. Moreover, comparison between the new model and two mentioned methods for the studied problem shows a remarkable agreement and reveals that the new model is more reliable in the point of view of optimality. A. Hosseinzadeh and S. A. Edalatpanah Copyright © 2016 A. Hosseinzadeh and S. A. Edalatpanah. All rights reserved. Research on Fuzzy Control Based Flexible Composite Winding System Mon, 15 Feb 2016 14:10:50 +0000 In the process of composite resin prepreg tape winding, the presence of pores or voids among the layers of composite can result in reduced strength of winding. To alleviate this problem, it is required that the composite tape winding machines be designed such that the layers of composite are evenly wound on the previous one. The paper presents a novel design of flexible winding system for composite tape winding. Based on the analysis of errors in winding process, the novel winding system eliminates winding point error and winding angle error based on the speed controlled flexible roller. This paper also presents the kinetic analysis of the novel system and its controller design. Experiments are conducted on the novel winding system. The experimental results illustrate that the novel flexible winding system has a good performance in winding accuracy. He Xiaodong, Shi Yaoyao, and Kang Chao Copyright © 2016 He Xiaodong et al. All rights reserved. A Modified FNN Fault Diagnosis on PCVD Microwave System Wed, 28 Oct 2015 07:44:22 +0000 A modified FNN fault diagnosis algorithm is presented in this paper for microwave subsystem of Plasma Chemical Vapor Deposition (PCVD). The symptom variables are selected as the crisp inputs, and the corresponding membership functions are obtained from premeasured data as well as experts’ diagnostic experience/knowledge. The prior probability and the restriction coefficients are combined into the FNN algorithm via matrix operator. This modified FNN algorithm is verified for PCVD fault diagnosis application and realizes the MIMO for multifault mode diagnosis. Zhenyu Li and Hongsheng Li Copyright © 2015 Zhenyu Li and Hongsheng Li. All rights reserved. Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO Tue, 27 Oct 2015 08:38:11 +0000 This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions. Abba Suganda Girsang, Chun-Wei Tsai, and Chu-Sing Yang Copyright © 2015 Abba Suganda Girsang et al. All rights reserved. A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System Mon, 28 Sep 2015 12:16:43 +0000 This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC. S. M. Odeh, A. M. Mora, M. N. Moreno, and J. J. Merelo Copyright © 2015 S. M. Odeh et al. All rights reserved. Quantitative Analyses and Development of a -Incrementation Algorithm for FCM with Tsallis Entropy Maximization Wed, 19 Aug 2015 09:03:05 +0000 Tsallis entropy is a -parameter extension of Shannon entropy. By extremizing the Tsallis entropy within the framework of fuzzy -means clustering (FCM), a membership function similar to the statistical mechanical distribution function is obtained. The Tsallis entropy-based DA-FCM algorithm was developed by combining it with the deterministic annealing (DA) method. One of the challenges of this method is to determine an appropriate initial annealing temperature and a value, according to the data distribution. This is complex, because the membership function changes its shape by decreasing the temperature or by increasing . Quantitative relationships between the temperature and are examined, and the results show that, in order to change equally, inverse changes must be made to the temperature and . Accordingly, in this paper, we propose and investigate two kinds of combinatorial methods for -incrementation and the reduction of temperature for use in the Tsallis entropy-based FCM. In the proposed methods, is defined as a function of the temperature. Experiments are performed using Fisher’s iris dataset, and the proposed methods are confirmed to determine an appropriate value in many cases. Makoto Yasuda Copyright © 2015 Makoto Yasuda. All rights reserved. Application of Fuzzy Optimization to the Orienteering Problem Mon, 13 Jul 2015 07:40:46 +0000 This paper deals with the orienteering problem (OP) which is a combination of two well-known problems (i.e., travelling salesman problem and the knapsack problem). OP is an NP-hard problem and is useful in appropriately modeling several challenging applications. As the parameters involved in these applications cannot be measured precisely, depicting them using crisp numbers is unrealistic. Further, the decision maker may be satisfied with graded satisfaction levels of solutions, which cannot be formulated using a crisp program. To deal with the above-stated two issues, we formulate the fuzzy orienteering problem (FOP) and provide a method to solve it. Here we state the two necessary conditions of OP of maximizing the total collected score and minimizing the time taken to traverse a path (within the specified time bound) as fuzzy goals and the remaining necessary conditions as crisp constraints. Using the max-min formulation of the fuzzy sets obtained from the fuzzy goals, we calculate the fuzzy decision sets ( and ) that contain the feasible paths and the desirable paths, respectively, along with the degrees to which they are acceptable. To efficiently solve large instances of FOP, we also present a parallel algorithm on CREW PRAM model. Madhushi Verma and K. K. Shukla Copyright © 2015 Madhushi Verma and K. K. Shukla. All rights reserved. New Closeness Coefficients for Fuzzy Similarity Based Fuzzy TOPSIS: An Approach Combining Fuzzy Entropy and Multidistance Tue, 23 Jun 2015 06:43:52 +0000 This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated. Mikael Collan, Mario Fedrizzi, and Pasi Luukka Copyright © 2015 Mikael Collan et al. All rights reserved. On Normalistic Vague Soft Groups and Normalistic Vague Soft Group Homomorphism Wed, 10 Jun 2015 12:51:55 +0000 We further develop the theory of vague soft groups by establishing the concept of normalistic vague soft groups and normalistic vague soft group homomorphism as a continuation to the notion of vague soft groups and vague soft homomorphism. The properties and structural characteristics of these concepts as well as the structures that are preserved under the normalistic vague soft group homomorphism are studied and discussed. Ganeshsree Selvachandran and Abdul Razak Salleh Copyright © 2015 Ganeshsree Selvachandran and Abdul Razak Salleh. All rights reserved. Fuzzy Clustering Using the Convex Hull as Geometrical Model Tue, 21 Apr 2015 10:42:12 +0000 A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms. Luca Liparulo, Andrea Proietti, and Massimo Panella Copyright © 2015 Luca Liparulo et al. All rights reserved. An Analytical Approach to Evaluating Nonmonotonic Functions of Fuzzy Numbers Tue, 31 Mar 2015 11:42:58 +0000 This paper presents a novel analytical approach to evaluating continuous, nonmonotonic functions of independent fuzzy numbers. The approach is based on a parametric -cut representation of fuzzy numbers and allows for the inclusion of parameter uncertainties into mathematical models. Arthur Seibel and Josef Schlattmann Copyright © 2015 Arthur Seibel and Josef Schlattmann. All rights reserved. Fuzzy Methods for Data Analysis Wed, 25 Mar 2015 06:30:18 +0000 Ferdinando Di Martino, Irina Perfilieva, and Salvatore Sessa Copyright © 2015 Ferdinando Di Martino et al. All rights reserved. A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices Mon, 23 Mar 2015 12:18:17 +0000 In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. However, in many such organizations, they often hesitate to publish their databases because of privacy and security issues although they believe the advantages of collaborative analysis. This paper proposes a novel collaborative framework for utilizing vertically partitioned cooccurrence matrices in fuzzy co-cluster structure estimation, in which cooccurrence information among objects and items is separately stored in several sites. In order to utilize such distributed data sets without fear of information leaks, a privacy preserving procedure is introduced to fuzzy clustering for categorical multivariate data (FCCM). Withholding each element of cooccurrence matrices, only object memberships are shared by multiple sites and their (implicit) joint co-cluster structures are revealed through an iterative clustering process. Several experimental results demonstrate that collaborative analysis can contribute to revealing global intrinsic co-cluster structures of separate matrices rather than individual site-wise analysis. The novel framework makes it possible for many private and public organizations to share common data structural knowledge without fear of information leaks. Katsuhiro Honda, Toshiya Oda, Daiji Tanaka, and Akira Notsu Copyright © 2015 Katsuhiro Honda et al. All rights reserved. Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms Mon, 23 Mar 2015 07:23:14 +0000 Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio. Arindam Chaudhuri Copyright © 2015 Arindam Chaudhuri. All rights reserved. Provenance Study of the Terracotta Army of Qin Shihuang’s Mausoleum by Fuzzy Cluster Analysis Thu, 19 Mar 2015 09:44:40 +0000 20 samples and 44 samples of terracotta warriors and horses from the 1st and 3rd pits of Qin Shihuang’s Mausoleum, 20 samples of clay near Qin’s Mausoleum, and 2 samples of Yaozhou porcelain bodies are obtained to determine the contents of 32 elements in each of them by neutron activation analysis (NAA). The NAA data are further analyzed using fuzzy cluster analysis to obtain the fuzzy cluster trend diagram. The analysis shows that the origins of the raw material of the terracotta warriors and horses from 1st and 3rd pits are not exactly the same but are closely related to the loam soil layer near Qin’s Mausoleum while distant from the loess layers in the same area and remotely related to the Yaozhou porcelain bodies. It can be concluded that the raw material of the terracotta warriors and horses was taken from certain loam layer near Qin’s Mausoleum and the kiln sites might be located nearby. Rongwu Li and Guoxia Li Copyright © 2015 Rongwu Li and Guoxia Li. All rights reserved. A Fuzzy Supplier Selection Application Using Large Survey Datasets of Delivery Performance Thu, 19 Mar 2015 09:06:01 +0000 A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. The values are derived from qualitative variables and expert opinion of membership in these variables found in industry survey data. The application is made to a supply chain management decision of supplier selection based upon delivery performance which is further divided into attributes that comprise this criterion. The algorithm allows multiple criteria to be considered for each decision parameter. Large sets of survey data regarding six suppliers in the electronic parts industry are gathered from over 150 purchasers and are analyzed through spreadsheet modeling of the fuzzy algorithm. The resulting decision support system allows supply chain managers to improve supplier selection decisions by applying fuzzy measures of criteria and associated beliefs across the dataset. The proposed model and method are highly adaptable to existing survey datasets, including datasets that have incomplete data, and can be implemented in organizations with low decision support resources, such as small and medium sized organizations. Jonathan Davis, Margaret F. Shipley, and Gary Stading Copyright © 2015 Jonathan Davis et al. All rights reserved. The I-V Characteristic Prediction of BCD LV pMOSFET Devices Based on an ANFIS-Based Methodology Tue, 17 Mar 2015 10:20:14 +0000 Comprehensive and predictive modeling of submicron devices using the traditional TCAD EDA tools of device simulation has become increasingly perplexing due to a lack of reliable models and difficulties in calibrating available device models. This paper proposes a new technique to model BCD submicron pMOSFET devices and to predict device behaviors under different bias conditions and different geometry dimensions by using the adaptive neurofuzzy inference system (ANFIS), which combines fuzzy theory and adaptive neuronetworking. Here, the power of using ANFIS to realize the I-V behaviors is demonstrated in these p-channel MOS transistors. After a systematic evaluation, it can be found that the predicting results of I-V behaviors of complicated submicron pMOSFETs by ANFIS are compared with the actual diagnostic experiment data, and a good agreement has been obtained. Furthermore, the error percentage was no greater than 2.5%. As such, the demonstrated benefits of this new proposed technique include precise prediction and easier implementation. Shen-Li Chen Copyright © 2015 Shen-Li Chen. All rights reserved. n-Tupled Fixed Points Theorem in Fuzzy Metric Spaces with Application Mon, 09 Mar 2015 07:35:29 +0000 We will introduce the concept of -tupled fixed points (for positive integer ) in fuzzy metric space by mild modification of the concept of -tupled fixed points (for even positive interger ) introduced by Imdad et al. (2013) in metric spaces. As application of the above-mentioned concept, we will establish some -tupled fixed point theorems for contractive type mappings in fuzzy metric space which extends the result of Roldán et al. (2013). Also we have given an application to solve a kind of Lipschitzian systems for variables and an integral system. P. P. Murthy and Rashmi Kenvat Copyright © 2015 P. P. Murthy and Rashmi Kenvat. All rights reserved. Discrete-Time Exponentially Stabilizing Fuzzy Sliding Mode Control via Lyapunov’s Method Wed, 25 Feb 2015 14:20:42 +0000 The exponentially stabilizing state feedback control algorithm is developed by Lyapunov’s second method leading to the variable structure system with chattering free sliding modes. Linear time-invariant discrete-time second order plant is considered and the control law is obtained by using a simple fuzzy controller. The analytical structure of the proposed controller is derived and used to prove exponential stability of sliding subspace. Essentially, the control algorithm drives the system from an arbitrary initial state to a prescribed so-called sliding subspace S, in finite time and with prescribed velocity estimate. Inside the sliding subspace S, the system is switched to the sliding mode regime and stays in it forever. The proposed algorithm is tested on the real system in practice, DC servo motor, and simulation and experimental results are given. Radiša Ž. Jovanović and Zoran M. Bučevac Copyright © 2015 Radiša Ž. Jovanović and Zoran M. Bučevac. All rights reserved.