A symbolic-neural method for solving control problems

Symbolic-neural processing is applied to the 'black box' problem, i.e., the synthesis of a system with a transfer function that adequately matches desired input-output relationships, by using many small neural networks, each capable of experimental training, coupled together through conventional logic paradigms. Explicit knowledge can then be encoded in the structure of the networks as well as in rules and algorithms. Learned knowledge is then input by 'showing' training data to the individual neural networks. The symbolic neural approach is a way of creating models which: (1) are easily completed quickly and have quality that can be improved over time; (2) are adaptive to environments for which they were not specifically programmed; and (3) accept human knowledge both in the form of explicit logic and in the form of experiential training.<
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