Intelligent engineering systems through artificial neural networks: proceedings of the Artificial Neural Networks in Engineering (ANNIE '95) conference, held November 12-15, 1995, in St. Louis, Missouri, U.S.A.. Fuzzy logic and evolutionary programming
Cihan H. Dagli
ASME Press, 1995 - Computers - 1034 pages
Proceedings of the Artificial Neural Networks in Engineering Conference, November 12-15, 1995 St. Louis, Missouri. Heightened interest in engineering applications of neutral networks in recent years has led to intense research in the field. Volume 5 of this highly successful series boasts the contribution of researchers from 20 countries. They examine the theory and applications of artificial neural networks, fuzzy logic, and evolutionary programming. The volume provides referred versions of the latest developments in design and manufacturing engineering, including comprehensive coverage of: Artificial neural network architecture; Fuzzy networks and systems; Evolutionary programming; Pattern recognition; Adaptive control; Smart engineering system design.
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The Capacity of Matcher Neural Networks
Multivalued Neural Associative Memories
Canonical Orders and Base Language of Fractals
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activation function adaptive analysis application approach approximation architecture Artificial Neural Networks associative memory backpropagation behavior binary classification cluster CMAC complex component Computer constraints convergence convex hull crossover data set defined determine developed domain dynamic Engineering equation error estimate evaluation example FALVQ Figure fuzzy logic fuzzy rules fuzzy set fuzzy system genetic algorithm genetic operators gradient descent hidden layer hidden units hyperboxes identified IEEE IEEE Trans implementation initial input vector iterations linear mapping Markov chain matrix membership functions method minimize module mutation neurons nonlinear objective function obtained operation optimal paper parameters patterns performance pixel population prediction presented problem processor proposed random recognition reliability represent samples scheme selected sensors shown shows sigmoid function signal simulation solution solve space step string structure Table technique training set transform updated variables wavelet weights