Accurate multi-step-ahead prediction of non-linear systems using the MLP neural network with spread encoding

Abstract

This paper focuses on the use of the standard multi-layer perceptron (MLP) neural network to provide accurate multi-step-ahead predictions of non-linear dynamical systems. A spread encoding method of representing continuous variables in a form suitable for presentation to an MLP is investigated. With this technique each numerical value is spread over the activity of several nodes at the inputs and outputs of the network. The main purpose of using spread encoding in this application is to form representations with sufficient accuracy to allow a neural network, trained using conventional feed-forward algorithms, to be used recursively. In this mode the network is required to predict the time evolution of the process output multiple time steps into the future, thus acting as a process model which has potential for improving control strategies that rely on a model of the plant and enhancing the performance of neural networks when used as simulation tools. The spread encoding form of data representation is compared to the conventional scaling method in an application of the MLP to modelling the response of a non-linear process. Results demonstrate that significant improvements in the neural network model prediction accuracy can be achieved using the spread encoding technique. The ability of the network model to capture the process dynamics is further illustrated by examining the localised frequency response of the network, in a novel application of spectral analysis techniques. The paper also includes introductory material on using neural networks for multi-step and single-step prediction.