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 programmingCihan H. Dagli As a follow-up to the previous four volumes of Intelligent Engineering Systems Through Artificial Neural Networks by the same editor, the present volume contains the edited versions of the technical presentations of ANNIE '95, held November, 1995 in St. Louis, Missouri. The 160-some contributions are grouped into six categories: artificial neural network architectures (including subsections on architectures and learning algorithms and training); fuzzy neural networks and systems; evolutionary programming; pattern recognition; adaptive control; and smart engineering system design (including bio-medical engineering systems; signal processing; forecasting; environmental applications; machining and robotics; process control, monitoring, and automated inspection; and general engineering). Includes bandw photographs, diagrams, and charts. Annotation copyright by Book News, Inc., Portland, OR |
Contents
Wavelet Packet Parallel Consensual Neural Networks | 5 |
The Capacity of Matcher Neural Networks | 15 |
Consequences of Limitation on Weights in Feedforward Multilayer Neural Networks | 21 |
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accuracy activation function adaptive analysis application approach approximation architecture Artificial Neural Networks backpropagation behavior classification cluster CMAC complex components computed constraints convergence convex hull correlation dimension data set defined detection determine developed dynamics Engineering equation error estimate evaluation example feature feedforward frequency fuzzy control fuzzy logic fuzzy rules fuzzy set genetic algorithm gradient descent hidden layer hidden units identified IEEE IEEE Trans implemented initial input vector iterations learning linear mapping Markov chain matrix measure membership functions method module multilayer perceptron mutation neural net neurons nonlinear number of hidden number of training obtained operation optimal output node paper parameters performance pixel prediction presented problem proposed recognition reliability represent robot samples scheme selected sensors shows sigmoid function signal simulation solution space step Table techniques training set transform update variables wavelet weights