Intelligent Engineering Systems Through Artificial Neural Networks: Proceedings of the Artificial Neural Networks in Engineering (ANNIE ... ) Conference, Volume 2ASME Press, 1992 - Intelligent control systems |
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Page 60
... Parallel Distributed Processing . Vols 1 & 2. M.I.T. Press , Cambridge , MA ( 1987 ) . 2. J.L. McClelland and D.E. Rumelhart . Explorations in Parallel Distributed Processing . M.I.T. Press , Cambridge , MA ( 1988 ) . 3. T. Triffet and ...
... Parallel Distributed Processing . Vols 1 & 2. M.I.T. Press , Cambridge , MA ( 1987 ) . 2. J.L. McClelland and D.E. Rumelhart . Explorations in Parallel Distributed Processing . M.I.T. Press , Cambridge , MA ( 1988 ) . 3. T. Triffet and ...
Page 127
... Parallel Development and Coding of Neural Feature Detectors . Biological ... Distributed Processing . A Handbook of Models , Programs , and Exercises ... Parallel Activation Models of Associative Memory . Proceedings Ninth International ...
... Parallel Development and Coding of Neural Feature Detectors . Biological ... Distributed Processing . A Handbook of Models , Programs , and Exercises ... Parallel Activation Models of Associative Memory . Proceedings Ninth International ...
Page 264
... processes exists [ 1 , 9 , 10 , 12 ] . Neural net learning processes capture the parallel nature of human cognition ; however ... Parallel distributed processing : Explorations in the microstructure of cognition . Vol . 1 : Foundations ...
... processes exists [ 1 , 9 , 10 , 12 ] . Neural net learning processes capture the parallel nature of human cognition ; however ... Parallel distributed processing : Explorations in the microstructure of cognition . Vol . 1 : Foundations ...
Contents
Design of Supervised Classifiers Using Boolean | 1 |
A Link Between | 15 |
A Neural NetworkBased Predictor for MIMO | 21 |
Copyright | |
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accuracy activation adaptive analysis application approach Artificial Neural Networks backpropagation binary classification cluster CMAC components Computer convergence data set defined defuzzification delta rule detection distribution dynamic Engineering equation error estimate evaluation experiments fault feature feedforward feedforward neural network fuzzy fuzzy logic fuzzy set given gradient gradient descent hidden layer hidden units Hopfield network hybrid identified IEEE implementation initial input layer input pattern input vector iterations Kohonen learning algorithm learning rate linear machine mapping matrix measure method minimize multilayer multilayer perceptron neocognitron neural net neurons noise nonlinear normalized obtained optimal output layer output node paper parameters pattern recognition perceptron performance prediction problem random represents robot rule samples selected sensor shown in Figure signal simulation solution space step structure supervised learning techniques texture threshold training set trajectory update values variables weight vector