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.
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TL;DR: This paper uses a multilayer feedforward ANN with the criterions of three people, each with different characteristic, using the backpropagation algorithm and different structures to rank a set of fuzzy numbers which can be considered as utilities of decision problems with fuzzy environment, hence enabling the best choice.
63 citations
08 Sep 1996
TL;DR: GARIC-Q is introduced, a new method for doing incremental dynamic programming using a society of intelligent agents which are controlled at the top level by fuzzy Q-learning and at the local level, each agent learns and operates based on GARIC.
Abstract: Fuzzy Q-learning, introduced earlier by the author, is an extension of Q-learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental dynamic programming using a society of intelligent agents which are controlled at the top level by fuzzy Q-learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of fuzzy Q-learning through generalization of input space by using fuzzy rules and bridges the gap between Q-learning and rule based intelligent systems.
63 citations
27 Nov 1995
TL;DR: The neuro-fuzzy modeling and learning mechanisms of CANFIS, wherein both neural networks and fuzzy systems play active roles together in an effort to reach a specific goal, are discussed.
Abstract: We discuss the neuro-fuzzy modeling and learning mechanisms of CANFIS (coactive neuro-fuzzy inference system) wherein both neural networks and fuzzy systems play active roles together in an effort to reach a specific goal. Their mutual dependence presents unexpected learning capabilities. CANFIS has extended the basic ideas of its predecessor ANFIS (adaptive network-based fuzzy inference system): the ANFIS concept has been extended to any number of input-output pairs. In addition, CANFIS yields advantages from nonlinear fuzzy rules. In light of some model-related limitations, this paper serves to highlight both neuro-fuzzy learning capacities and practical obstacles encountered in performing neuro-fuzzy modeling.
63 citations
TL;DR: This paper presents different approaches to the problem of fuzzy rules extraction by using a combination of fuzzy clustering and genetic algorithms as the main tools, and defines a hybrid system by which they can have different approaches in a fuzzy modeling process.
63 citations
TL;DR: Control theoretic analysis of a fuzzy control system is presented in the sense of Lyapunov and gives an account of the relationship between control performance and the design parameters of the FLC, which has been obscure in the theory of fuzzy control.
Abstract: Based on the similarity between prevalent fuzzy logic controllers (FLC) and the conventional robust controller, i.e., the variable structure controller, control theoretic analysis of a fuzzy control system is presented in the sense of Lyapunov. As well as the robustness of the fuzzy control system against uncertainties of a controlled process, this analysis gives an account of the relationship between control performance and the design parameters of the FLC, which has been obscure in the theory of fuzzy control.
63 citations