Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: Experimental results on high-dimensional benchmark problems have shown that by using the proposed scheme the most influential fuzzy rules can be effectively induced and selected, and at the same time feature ranking results can also be obtained to construct parsimonious fuzzy classifiers with better generalization performance than the well-known algorithms in literature.
Abstract: In this paper, a new scheme for constructing parsimonious fuzzy classifiers is proposed based on the L2-support vector machine (L2-SVM) technique with model selection and feature ranking performed simultaneously in an integrated manner, in which fuzzy rules are optimally generated from data by L2-SVM learning. In order to identify the most influential fuzzy rules induced from the SVM learning, two novel indexes for fuzzy rule ranking are proposed and named as alpha-values and omega-values of fuzzy rules in this paper. The alpha-values are defined as the Lagrangian multipliers of the L2-SVM and adopted to evaluate the output contribution of fuzzy rules, while the omega-values are developed by considering both the rule base structure and the output contribution of fuzzy rules. As a prototype-based classifier, the L2-SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. Experimental results on high-dimensional benchmark problems have shown that by using the proposed scheme the most influential fuzzy rules can be effectively induced and selected, and at the same time feature ranking results can also be obtained to construct parsimonious fuzzy classifiers with better generalization performance than the well-known algorithms in literature.
78 citations
TL;DR: From the simulation of ball and beam control system, it is demonstrated that the proposed scheme approximates with good accuracy the model nonlinear controller with fewer fuzzy rules than the centralized fuzzy system and its control performance is comparable to that of the non linear controller.
78 citations
TL;DR: This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models by using the approximation capability of T-S fuzzy models.
Abstract: This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. By using the approximation capability of T-S fuzzy models, the nonlinear systems are expressed by uncertain T-S fuzzy models with norm-bounded approximation errors. A novel fuzzy dynamic integral sliding-mode control (DISMC) scheme is then developed for the nonlinear systems based on their T-S fuzzy approximation models. One of the key features of the new DISMC scheme is that the restrictive assumption that all local linear systems share a common input matrix, which is required in most existing fuzzy integral sliding-mode control (ISMC) approaches, is removed. Furthermore, the results of universal fuzzy ISMCs for two classes of nonlinear systems, along with constructive procedures to obtain the universal fuzzy ISMCs, are provided, respectively. Finally, the advantages and effectiveness of the proposed approaches are illustrated via a numerical example.
78 citations
TL;DR: Some aspects of manipulation of fuzzy data with the aid of fuzzy relational equations are considered and two stages of manipulation process are indicated: combining pieces of evidence and inferring their mutual correspondence.
78 citations
TL;DR: The development of a novel soft computing approach to model the supervisor of manufacturing systems is described, it is named Fuzzy Cognitive Maps (FCMs) and it is used tomodel the behaviour of complex systems.
Abstract: The development of a novel soft computing approach to model the supervisor of manufacturing systems is described, it is named Fuzzy Cognitive Maps (FCMs) and it is used to model the behaviour of complex systems. Fuzzy cognitive maps combine characteristics of both fuzzy logic and neural networks. The description and the construction of fuzzy cognitive maps are examined, a new methodology for developing fuzzy cognitive maps is proposed here and as an example the fuzzy cognitive map for a simple plant is developed. A hierarchical two-level structure for supervision of manufacturing systems is presented, where the supervisor is modelled as a fuzzy cognitive map. The fuzzy cognitive map model for the failure diagnosis part of the supervisor for a simple chemical process is constructed.
78 citations