Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence/Book and Disk
Written by one of the foremost experts in the field of neural networks, this is the first book to combine the theories and applications or neural networks and fuzzy systems. The book is divided into three sections: Neural Network Theory, Neural Network Applications, and Fuzzy Theory and Applications. It describes how neural networks can be used in applications such as: signal and image processing, function estimation, robotics and control, analog VLSI and optical hardware design; and concludes with a presentation of the new geometric theory of fuzzy sets, systems, and associative memories.
ACTIVATIONS AND SIGNALS
Additive Activation Models
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ABAM activation adaptive approximation associative memory backpropagation behavior binary BIOFAM bipolar bit vector bivalent centroid competitive learning competitive learning law computational control surface convergence correlation-product corresponds defines denotes deterministic differential competitive learning differential Hebbian dynamical system encoding equals equilibrium error estimate exponentially FAM bank FAM cells FAM rules FAM system FAM-rule feedback feedforward field Fx Figure fuzzy controller fuzzy sets fuzzy subsets fuzzy systems fuzzy variables fuzzy-set values Gaussian global Grossberg Hebbian learning Hopfield hyperrectangle indicator function inference input input-output iteration Kosko linear Lyapunov function maps matrix mean-squared measure menu neural networks neurons noise nonfuzzy nonlinear option output fuzzy set parameters pattern pendulum positive probability density function problems product space pulse pulse-coded quantization RABAM random variables random vector represent signal functions signal Hebbian stability stochastic stochastic approximation subimage supervised synaptic vectors theory threshold trajectories truck unsupervised learning velocity weight zero