Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications
Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design.
Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another.
This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include:
Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.
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Introduction to Neural Networks Fuzzy
A New FuzzyNeural Controller
Expert KnowledgeBased Direct Frequency
Design of an ElectroHydraulic System Using
Neural Fuzzy Based Intelligent Systems and
Vehicle Routing through Simulation of Natural
Fuzzy Logic and Neural Networks in Fault
accuracy actuator analysis application approach Artificial Neural Networks ATM Networks behavior calls and services cell cepstrum chromosomes commutation frequency complex configuration connection control system converter converter's corresponding crossover current space vectors customers defined defuzzification described engine Equation error estimation evolutionary algorithms example fault detection fault diagnosis filter fitness function fuzzy controller fuzzy logic fuzzy rules fuzzy sets Fuzzy Systems Genetic Algorithms heuristic hidden layer IEEE Trans input current input line currents input vector knowledge-based learning matrix mechanism membership functions method motor NeuFuz Neural Fuzzy neural net neural nets neuro-fuzzy neurons nodes nonlinear optimization output layer output line performance piston policer population position Proc recurrent represents residual evaluation rules and membership schedule selection shown in Figure signal simulation solution speech speed string switching TDNN techniques topology traffic transfer function variable vehicle routing problems visemes weight XDFC