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System Design with GNUs
A Training Strategy and Functionality Analysis
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addressed sites Aleksander analogue architecture associative memory attack automaton back-propagation Barto behaviour biological bits Boolean functions buffer clusters computability contents convergence defined desired output deterministic Digital Multi-Layer Neural digital neural discrimination DMLN effect example feed-forward feedback functional capacity generalisation Gorse and Taylor Hamming distance i-pRAM IEEE implementation including sea mail input pattern input variables language layer learning algorithm machine memory units method Multi-Layer Neural Networks Myers N-tuple neural models neural nets neurons noise Octopus Vulgaris operation optic lobe OVSIM parameter pattern recognition PLN networks possible pRAM pRAM nets presented probabilistic automata Probabilistic Logic probability problem production rule pyramid RAM-based random random access memory regular grammar regular language reinforcement response S-model self-organising shown in Figure stimulus stochastic Stonham stored techniques threshold topology training algorithm training patterns training set vector vertical lobe visual WISARD wjSj