Topic
Membership function
About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.
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TL;DR: An indicator model for evaluating trends in river quality using a two-stage fuzzy set theory to condense efficiently monitoring data is proposed and provides a more sensitive indication of changes in quality than the RPI.
118 citations
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TL;DR: A new method to automatically learn the Knowledge Base of a Fuzzy Rule-Based System is proposed by finding an appropriate Data Base using a Genetic Algorithm and considering a simple generation method to derive the Rule Base.
118 citations
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01 Jan 2008TL;DR: It is shown by the validation applications that the developed evolving T-S fuzzy model can identify the nonlinear system satisfactorily with acceptable number of rules and appropriate inputs.
Abstract: In this paper, a new encoding scheme is presented for learning the Takagi-Sugeno (T-S) fuzzy model from data by genetic algorithms (GAs). In the proposed encoding scheme, the rule structure (selection of rules and number of rules), the input structure (selection of inputs and number of inputs), and the antecedent membership function (MF) parameters of the T-S fuzzy model are all represented in one chromosome and evolved together such that the optimisation of rule structure, input structure, and MF parameters can be achieved simultaneously. The performance of the developed evolving T-S fuzzy model is first validated by studying the benchmark Box-Jenkins nonlinear system identification problem and nonlinear plant modelling problem, and comparing the obtained results with other existing results. Then, it is applied to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper of which identification problem is significantly difficult due to its inherently hysteretic and highly nonlinear dynamics. It is shown by the validation applications that the developed evolving T-S fuzzy model can identify the nonlinear system satisfactorily with acceptable number of rules and appropriate inputs.
117 citations
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TL;DR: An encompassing, self-contained introduction to the foundations of the broad field of fuzzy clustering is presented, with special emphasis on the interpretation of the two most encountered types of gradual cluster assignments: the fuzzy and the possibilistic membership degrees.
117 citations
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TL;DR: The fuzzy set theory and the basic nature of subjectivity due to the ambiguity are incorporated to achieve a flexible decision approach suitable for uncertain and fuzzy environment.
117 citations