Journal of Intelligent & Fuzzy Systems - Volume 26, issue 4

Authors: Sedaghati, Reza | Kavousi-Fard, Abdollah

Article Type: Research Article

Abstract: This paper proposes a new stochastic framework based on point estimate method to solve the optimal operation management of Distribution Feeder Reconfiguration (DFR) considering several Wind Turbines (WTs) in the system. The proposed method can properly solve the complex and discrete DFR optimization problem by the use of an adaptive modification approach based on firefly algorithm (FA). In addition, a new stochastic solution based on 2m Point Estimate Method (2m PEM) is proposed to handle the uncertainty associated with the problem random variables including the active and reactive loads as well as the wind speed variations effectively. The problem is …then formulated in a multi-objective optimization structure including four significant targets: 1) active power losses, 2) bus voltage deviation, 3) total system costs and 4) total pollution produced. As a result of the conflicting behavior of the four objective functions, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The feasibility and satisfying performance of the proposed method is examined on the IEEE 32-bus standard test system. Show more

Keywords: Stochastic reconfiguration problem, uncertainty, multi-objective optimization, wind turbine, modified FA

DOI: 10.3233/IFS-130850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1711-1721, 2014

Authors: Mon, Yi-Jen | Lin, Chih-Min

Article Type: Research Article

Abstract: An adaptive terminal sliding-mode recurrent fuzzy neural network (ATSRFNN) control system is developed to control a coupled double inverted pendulum system. The proposed ATSRFNN control system is composed of a recurrent fuzzy neural network (RFNN) controller and an adaptive terminal sliding (ATS) controller. The RFNN controller is designed to mimic an ideal controller, and the ATS controller is designed to cope with the approximation error and external disturbance. The simulation results show the proposed ATSRFNN control system can achieve better control performance and robustness in comparison with a hierarchical fuzzy sliding-mode control system.

Keywords: Recurrent fuzzy neural network, adaptive terminal sliding mode control, double inverted pendulum system

DOI: 10.3233/IFS-130851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1723-1729, 2014

Authors: Ou, Ou | Zhang, Hongbin | Yu, Guanjie | Guo, Xiansheng | Dang, Chuangyin

Article Type: Research Article

Abstract: This paper is concerned with the stability analysis and H∞ fuzzy controller design of continuous-time fuzzy interconnected systems with time-varying delays. The fuzzy interconnected system consists of N interconnected time-delay Takagi-Sugeno(T-S) fuzzy subsystems and the delay-dependent stability analysis is based on the fuzzy weighting-dependent Lyapunov-Krasovskii functionals. It is shown that the delay-dependent stability of the time-delay fuzzy interconnected systems can be established if a fuzzy weighting-dependent Lyapunov-Krasovskii functional can be constructed, and moreover, the functional can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible. The decentralized H∞ controllers are also designed …by solving a set of LMIs based on these fuzzy weighting-dependent Lyapunov-Krasovskii functionals. It is demonstrated via numerical examples that the stability and controller synthesis results based on fuzzy weighting-dependent Lyapunov-Krasovskii functionals are less conservative than those based on the common quadratic Lyapunov-Krasovskii functionals. Show more

Keywords: Fuzzy control, interconnected systems, $H_{\infty}$ control, time-delay, linear matrix inequalities (LMIs)

DOI: 10.3233/IFS-130852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1731-1744, 2014

Authors: Sojodishijani, Omid | Ramli, Abdul Rahman

Article Type: Research Article

Abstract: This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional feature space. While there are adaptive forms of feature extraction methods, which transform training patterns to low-dimensional space and/or improve classifier accuracy, they are vulnerable to nonparametric changes in data and must continuously update their parameters. In the proposed method, an optimal transformation matrix transforms time-labeled instances from the original space to a new feature space to maximize the probability of selecting the correct class label for …incoming instances using similarity-based classifiers. To this end, for a given time-labeled instance, nonparametric intra-class and extra-class distributions are proposed. The proposed method is also furnished to a temporal detector to provide the most convenient time for the adaptation phase. Experimental results on real and synthesized datasets that include real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments. Show more

Keywords: Just-in-time adaptive component analysis, adaptive classification, nonparametric data stream processing, similarity-based feature extraction

DOI: 10.3233/IFS-130853

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1745-1758, 2014

Authors: Huang, Shasha | Li, Qingguo

Article Type: Research Article

Abstract: We present an infrastructure for describing and handling uncertainty and vagueness in a combination of Description Logics and rules in the Semantic Web. More concretely, we apply fuzzy set theory to hybrid MKNF knowledge bases, and establish fuzzy hybrid MKNF knowledge bases, which is sound with respect to the classical two-valued semantics. Furthermore, we discuss some fundamental properties of fuzzy semantics, and characterize fuzzy MKNF models of general MKNF knowledge bases. We also provide characterizations of fuzzy MKNF models of positive and stratified MKNF knowledge bases in terms of a fixpoint operator and an iterative fixpoint semantics, respectively. Finally, we …describe a special case of fuzzy nondisjunctive positive MKNF knowledge base, in which the truth value of a modal atom can be computed in polynomial time in the data complexity. Show more

Keywords: Knowledge representation, description logics, fuzzy logic, logic programming, fixpoint procedure

DOI: 10.3233/IFS-130854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1759-1770, 2014

Authors: Saad Saoud, Lyes | Rahmoune, Fayçal | Tourtchine, Victor | Baddari, Kamel

Article Type: Research Article

Abstract: In this paper, the development of an improved Takagi Sugeno (TS) fuzzy model for identification and chaotic time series prediction of nonlinear dynamical systems is proposed. This model combines the advantages of fuzzy systems and Infinite Impulse Response (IIR) filters, which are autoregressive moving average models, to create internal dynamics with just the control input. The structure of Fuzzy Infinite Impulse Response (FIIR) is presented, and its learning algorithm is described. In the proposed model, the Butterworth analogue prototype filters are estimated using the obtained membership functions. Based on the founding orders of the analogue filters, the IIR filters could …be constructed. The IIR filters are introduced to each TS fuzzy rule which produces local dynamics. Gustafson–Kessel (GK) clustering algorithm is used to generate the clusters which will be used to find the number of the IIR parameters for each rule. The hybrid genetic algorithm and simplex method are used to identify the consequence parameters. The stability of the obtained model is studied. To demonstrate the performance of this modeling method, three examples have been chosen. Comparative results between the FIIR model on one hand, and the traditional TS fuzzy model, the neural networks and the neuro-fuzzy network on the other hand. The results show that the proposed method provides promising identification results. Show more

Keywords: TS fuzzy models, IIR filters, identification, prediction, photovoltaic module

DOI: 10.3233/IFS-130856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1771-1785, 2014

Authors: Zhou, Wei

Article Type: Research Article

Abstract: A crucial issue related to the Atanassov's intuitionistic fuzzy operators is how to determine their weights. Various weighted methods have been proposed last decades, but it seems that there is no investigation on the monotonous and proportion-invariant properties, which is decisive for aggregation and comparison of Atanassov's intuitionistic fuzzy values in group decision making. In this paper, we propose a novel weighted method, i.e., precisely weighted method, to calculate Atanassov's intuitionistic fuzzy aggregation operator weights, and prove its monotonicity and proportion-invariance. Then, we develop two weighted aggregation operators based on this new method, i.e. the Atanassov's intuitionistic fuzzy ordered precisely …weighted averaging (A-IFOPWA) operator and the Atanassov's intuitionistic fuzzy ordered precisely weighted geometric (A-IFOPWG) operator. Furthermore, some of their desirable properties are investigated in detail. Finally, a practical example is provided to illustrate the precisely weighted method and the developed aggregation operators. Show more

Keywords: Atanassov's intuitionistic fuzzy value, weighted method, A-IFOPWA operator, A-IFOPWG operator, group decision making

DOI: 10.3233/IFS-130858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1787-1798, 2014

Authors: Xu, Weihua | Liu, Yufeng | Sun, Wenxin

Article Type: Research Article

Abstract: Atanassov's intuitionistic fuzzy 𝒯 equivalence information systems are natural extensions of fuzzy 𝒯 equivalence information systems. The aim of this paper is to investigate the uncertainty measures of knowledge in Atanassov's intuitionistic fuzzy 𝒯 equivalence information systems. At the first, we introduce the concepts of knowledge granulation, knowledge entropy and knowledge uncertainty measure in Atanassov's intuitionistic fuzzy 𝒯 equivalence information systems, and some important properties of them are studied. From these properties, it can be shown that these measures provide important approaches to measuring the discernibility ability of different knowledge in Atanassov's intuitionistic fuzzy 𝒯 equivalence information systems. And relationships …among knowledge granulation, knowledge entropy and knowledge uncertainty measure are considered. Furthermore, we introduce the definition of rough entropy of rough sets in Atanassov's intuitionistic fuzzy 𝒯 equivalence information systems. By an example, it is shown that the rough entropy of rough set is more accurate than natural extension of classical rough degree to measure the roughness of rough set in Atanassov's intuitionistic fuzzy 𝒯 equivalence information systems. Show more

Keywords: Information system, Atanassov's intuitionistic fuzzy 𝒯 equivalence relation, Atanassov's intuitionistic fuzzy rough sets, uncertainty measure

DOI: 10.3233/IFS-130859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1799-1811, 2014

Authors: Shobkolaei, Nabi | Sedghi, Shaban | Roshan, Jamal R. | Altun, Ishak

Article Type: Research Article

Abstract: The purpose of this paper is to prove a related fixed point in S-complete Hausdorff uniform space. For this we use the concept of E-distance, which is introduced by Aamri and El-Moutawakil [1, 2], in uniform space and some metric versions of related fixed point theorems.

Keywords: Uniform spaces, related fixed point

DOI: 10.3233/IFS-130860

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1813-1816, 2014

Authors: Behzadi, Sh.S. | Allahviranloo, T. | Abbasbandy, S.

Article Type: Research Article

Abstract: In this paper, the fuzzy Fast Galerkin method (FFGM) and the fuzzy collocation method (FCM) have been used to solve the fuzzy linear Volterra-Fredholm integral equation of the second kind (FVFIE – 2). We convert a fuzzy linear Volterra-Fredholm integral equation to a system of fuzzy equations by using FFGM and FCM. The existence and uniqueness of the solution and convergence of the proposed method are proved in detail. Finally some examples show the accuracy of these methods.

Keywords: Fuzzy number, Volterra-Fredholm integral equations, Fuzzy Galerkin method, Fuzzy collocation method

DOI: 10.3233/IFS-130861

Citation: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1817-1822, 2014