A universal structure for artificial neural networks

An approximation procedure, named successive linearization, is introduced for unified implementation of a large class of neural networks. A nonlinear neural model with dynamic sensitivity is presented. It is modular and has rapid learning schemes. Arbitrary nonlinear functions with memory which are commonly used for modeling dynamical systems, as well as static nonlinear classification boundaries, can both be implemented equally well. Fast learning algorithms for the universal structure are presented.<
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