Smart Engineering Systems: Neural Networks, Fuzzy Logic, Data Mining, and Evolutionary Programming : Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE '97), Held November 9-12, 1997, in St. Louis, Missouri, U.S.A.
Cihan H. Dagli
ASME Press, 1997 - Computers - 1078 pages
Proceedings of the Artificial Neural Networks in Engineering Conference, November 9-12, 1997, St. Louis, Missouri. The papers compiled in this book focus on building smart components to engineering systems currently available. The term smart in this context indicates physical systems that can interact with their environment and adapt to changes in both space and time by their ability to manipulate the environment through self-awareness and perceived models of the world based on both quantitative and qualitative information. Recent technologies such as artificial neural networks, fuzzy logic, evolutionary programming, data mining wavelets, complex systems, and virtual reality form the basis of Smart Engineering System Design. In 1997, the Department of Engineering Management at the University of Missouri-Rolla organized the ANNIE'97 conference, to advance the techniques of Smart Engineering Sustem Design in collaboration with the IEEE Neural Network Council. This was the seventh meeting held in St. Louis,Missouri,U.S.A, since the founding of the conference in 1991. The conference attracted over 162 papers from 20 countries, which, after being peer-reviewed and revised, have been included in this book.
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Adaptive MultiModule Approximation Network
Toward Dynamic Adaptation of Receptive Field Properties of Artificial
Radical Pruning Methods for Classification Radial Basis Function
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action activation adaptive agents algorithm analysis applied approach approximation architecture average behavior block calculated called classification combination compared complex component Computer considered consists corresponding decision defined described detection determined dynamic edge effective elements Engineering environment equation error estimate evaluation example experiments Figure fitness function fuzzy Genetic Algorithms given global hidden identified IEEE implemented important improve increase initial input INTRODUCTION layer learning limited machine mapping means measure membership method neural network neurons node object obtained operator optimal original output parameters pattern performance population position possible prediction presented problem procedure proposed range recognition REFERENCES region represents robot rules selected sensor shown shows signal simple simulation solution solve space step string structure surface Table technique transformation units University variables vector weights