Learning procedure for multi-level neural network

Abstract

A reiterative learning procedure with training and test processes for a binary supervised neural network includes at least an error signal generator for weighting factor updating in the training process, which generates an error signal that is perturbed in polarity and amplitude in the difference derived by subtracting an output unit signal from corresponding binary teacher signal and then generates the difference as an error signal after a maximum absolute value of differences among erroneous binary output signals has become smaller than a threshold once. A training signal memory stores a set of training signals and adds test signals providing erroneous binary output signals that are transferred from a test signal memory in the test process to the set of training input signals as incremental training input signals. An affordable signal memory stores input signals with sufficiently large margins providing correct binary output signals that are transferred from the training signal memory in the training process and the test signal memory in the test process. The reiterative learning procedure, with minimum necessary training and test input signals and control of the error perturbation in the training process, can provide a binary space to obtain a desired binary output, and also realizes an extremely high generalization ability.