An adaptively trained neural network

Abstract

A training procedure that adapts the weights of a trained layered perceptron artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The resulting adaptively trained neural network (ATNN), based on nonlinear programming techniques, is shown to adapt to new training data that are in conflict with earlier training data without affecting the neural networks' response to data elsewhere. The adaptive training procedure also allows for new data to be weighted in terms of its significance. The adaptive algorithm is applied to the problem of electric load forecasting and is shown to outperform the conventionally trained layered perceptron.