Authors: Zongyan Li; Matt Best
Addresses: Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK ' Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK
Abstract: This paper presents an optimisation method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilising the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. Applications of vehicle handling and ride model identification are presented in this paper to demonstrate the optimisation technique, and the optimal input layer structure and the optimal number of neurons for the NN models are investigated. The results show that the developed NN model requires significantly fewer coefficients and less training time while maintaining high simulation accuracy compared with that of the unoptimised model.
Keywords: optimisation; correlation analysis; neural networks; F-ratio; mean squared error; MSE; nonlinear autoregressive exogenous; NARX; Levenberg-Marquardt; input channels; simulation; vehicle handling; ride model identification.
International Journal of Modelling, Identification and Control, 2016 Vol.25 No.3, pp.217 - 226
Available online: 06 Apr 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article