Abstract
Introduces a learning algorithm for multi-layered feedforward network-weight evolution algorithm with deterministic perturbation. During the learning phase of the gradient algorithm (such as backpropagation), the network weights are adjusted intentionally in order to have an improvement in system performance. The intention is to reduce the overall system error after every weight update. By looking at the error component, it is possible to adjust some of the network weights dramatically so as to have an overall reduction in system error. Using the deterministic perturbation, it is found that the weight evolution between the hidden and output layer can accelerate the convergence speed, whereas the weight evolution between the input layer and the hidden layer can assist in solving the local minima problem.
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