A self-training, self-repairing back-propagation environment

The author introduces a series of novel approaches to backpropagation: (1) the use of logic forms (classical, modal, and nonmonotonic) as training tools; (2) the construction of new nets through the responses of logically trained nets (weight sets); (3) the use of N2 as a reset mechanism for impermissibly slow or false responses by subnets; and (4) the retraining of failing subnets by the logically trained nets. A biologically plausible basis for the system is offered.<
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