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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: A state-of-charge (SOC) estimation system for the lead-acid battery, which is free from the time-dependent variation of the battery characteristics, is developed by using an improved Coulomb metric method and the learning system uses the fuzzy logic.
103 citations
TL;DR: The linear fuzzy controller is precisely equivalent to a linear non-fuzzy PI controller if mixed fuzzy logic is used to evaluate the control rules, when the fuzzy logics used are selected with due regard to prior associations implied by the control rule operands themselves.
103 citations
TL;DR: It is obtained that the lattice of fuzzy ideals is isomorphic to the lattices of fuzzy congruence on a generalized Boolean algebra and the products of fuzzy ideal are considered.
103 citations
TL;DR: A systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model that encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles is introduced.
Abstract: Introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, non-noisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.
103 citations
TL;DR: In this paper, the authors combine fuzzy logic and fuzzy set theory to extend binary morphology to grey-scale images, which gives rise to a large class of morphological operators for grey scale images, including Minkowski addition and subtraction.
Abstract: There exist several methods to extend binary morphology to grey-scale images. One of these methods is based on fuzzy logic and fuzzy set theory. Another approach starts from the complete lattice framework for morphology and the theory of adjunctions. In this paper, both approaches are combined. The basic idea is to use (fuzzy) conjunctions and implications which are adjoint in the definition of dilations and erosions, respectively. This gives rise to a large class of morphological operators for grey-scale images. It turns out that this class includes the often used grey-scale Minkowski addition and subtraction.
103 citations