Optimization-of-the-Input-Layer-Structure-for-Feed-Forward-Narx-Neural-Networks.pdf (258.91 kB)
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Optimisation of the input layer structure for feed-forward NARX neural network

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conference contribution
posted on 29.04.2015, 13:07 by Zongyan Li, Matt BestMatt Best
This paper presents an optimization 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 utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

XIII International Conference on Modelling, Identification and Control Engineering

Volume

1

Issue

1

Pages

1 - 6 (6)

Citation

LI, Z. and BEST, M.C., 2015. Optimisation of the Input Layer Structure for Feed-forward NARX Neural Network. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering 9 (7), pp. 527-532.

Publisher

© World Academy of Science Engineering and Technology (WASET)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Publication date

2015

Notes

This paper was presented at: ICMICE 2015: 17th International Conference on Modelling, Identification and Control Engineering, 9th-10th July 2015, Prague, Czech Republic.

Language

en

Location

Prague, Czech Republic