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Chp2.pdf

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 NEURAL NETWORK STRUCTURES ã McCullcoch-Pitts Model ã Unipolar/bipolar case ã Linear Separation ã Logic Functions ã Basic Limitation : EXOR Function ã General Case ã Multilayer Structures : Feedforward Case ã Feedback Structures : Memory inputs weights nonlinear block output Basic assumptions : (unipolar case) ã Simple extension : (bipolar case) v=0 input space ã Basic logic operations are LINEARLY SEPARABLE ã could be realized by McCulloch-Pitts neuron! UNIPOLAR CASE (
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    NEURAL NETWORK STRUCTURES ã  McCullcoch-Pitts Model ã  Unipolar/bipolar case ã  Linear Separation ã Logic Functions ã  Basic Limitation : EXOR Function ã  General Case ã Multilayer Structures : Feedforward Case ã  Feedback Structures : Memory  inputs weights nonlinear block output  Basic assumptions : (unipolar case) ã Simple extension : (bipolar case)  v=0 input space

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