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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications

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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:odirect frequency convertersoelectro-hydraulic systemsomotor controlotoaster controlospeech recognitionovehicle routingofault diagnosisoAsynchronous Transfer Mode (ATM) communications networksotelephones for hard-of-hearing peopleocontrol of gas turbine aero-enginesotelecommunications systems designFusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Artificial neural networks are being applied to a wide variety of automation problems including adaptive
control, optimization, medical diagnosis, decision making, as well as information and signal processing,
including speech processing. ANNs have proven to be very suitable for processing sensor data, in particular,
feature extraction and automated recognition of signals and multi-dimensional objects. Pattern recognition
has, however, emerged as a major application because the network structure is suited to tasks that biological
systems perform well, and pattern recognition is a good example where biological systems out-perform
traditional rule-based artificial intelligence approaches.
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