| 摘要 |
An Artificial Neural Network ( 110 ) includes a hidden layer ( 209 ) of distance metric computer nodes ( 210, 214, 218 ) that evaluate distances of a input vector from metric space centers, an additional layer of adaptable infinite logic aggregators ( 236, 240, 244 ) that combine the per-unit distance output values by the distance metric computer nodes ( 210, 214, 218 ) using adaptable infinite logic. In certain embodiments the adaptable infinite logic aggregators include veracity signal pre-processors ( 602, 702 ) that can be configured to make inferences in a continuum from positive to negative including no inference from each distance and infinite logic connective signal processors ( 604, 702 ) that can implement a continuum of functions covering the range of fuzzy logic union operators, fuzzy logic intersection operators, and all linear and nonlinear averaging operators between them. Control parameters (e.g., alpha<SUB>i</SUB>, beta<SUB>i</SUB>, lambda<SUB>A</SUB>, lambda<SUB>D</SUB>, w<SUB>i</SUB>) of the distance metric computer nodes and adaptable infinite logic aggregators can be determined by direct search optimization, using training data.
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