Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations.
Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications:
Suitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field.
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activation functions Adaptive Fuzzy Controllers ANFIS applications artificial neural networks atomic factors chapter coefficients comparison computation concept control ability CVA patients decision tree defined defuzzification denoted description frame discussed enhancement nodes Equation example Expression factor f factor space cane family of factors feedforward neural network flexible arm fuzzy learning fuzzy logic fuzzy model fuzzy neural network fuzzy sets fuzzy statistical Fuzzy Systems given identity function IEEE IEEE Transactions inhibitory input variables interpolation function learning algorithm linear linear programming mapping mathematical means membership functions method model of neurons monotonic neuro-fuzzy neurons nonlinear normal object output parameters PID controller piecewise prediction problem Proof Proposition pseudoinverse representation extension result Robotics rule base satisfies shown in Figure ſhte sigmoid function simplified fuzzy control Step subfactor sufficient with respect Sugeno switch factor t-norm tactile sensor Theorem time-series training data update vector weight matrix