Structure optimisation of input layer for feed-forward NARX neural network Online publication date: Wed, 06-Apr-2016
by Zongyan Li; Matt Best
International Journal of Modelling, Identification and Control (IJMIC), Vol. 25, No. 3, 2016
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com