Neuro-fuzzy

About: Neuro-fuzzy is a research topic. Over the lifetime, 23096 publications have been published within this topic receiving 526078 citations.

Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis

Abstract: This paper describes an application of fuzzy logic in designing controllers for industrial plants. A fuzzy logic is used to synthesize linguistic control protocol of a skilled operator. The method has been applied to pilot scale plants as well as in practical situations. The merits of this method and its usefulness to control engineering are discussed. An avenue for further work in this area is described where the need is to go beyond a purely descriptive approach, and means for implementing a prescriptive or a self-organizing system are explored.

Fuzzy clustering with a fuzzy covariance matrix

Abstract: A class of fuzzy ISODATA clustering algorithms has been developed previously which includes fuzzy means. This class of algorithms is generalized to include fuzzy covariances. The resulting algorithm closely resembles maximum likelihood estimation of mixture densities. It is argued that use of fuzzy covariances is a natural approach to fuzzy clustering. Experimental results are presented which indicate that more accurate clustering may be obtained by using fuzzy covariances.

Expected value of fuzzy variable and fuzzy expected value models

Abstract: This paper will present a novel concept of expected values of fuzzy variables, which is essentially a type of Choquet integral and coincides with that of random variables. In order to calculate the expected value of general fuzzy variable, a fuzzy simulation technique is also designed. Finally, we construct a spectrum of fuzzy expected value models, and integrate fuzzy simulation, neural network, and genetic algorithms to produce a hybrid intelligent algorithm for solving general fuzzy expected value models.

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems

Abstract: Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems It includes Matlab software, with a Neural Network Toolkit, and a Fuzzy System Toolkit