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Optimization of the input layer structure for feed-forward NARX neural network

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journal contribution
posted on 22.09.2016 by Zongyan Li, Matt 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.

Funding

This work was supported by Jaguar Land Rover and the UK-EPSRC grant EP/xxxxxxx/x as part of the jointly funded Programme for Simulation Innovation.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering

Volume

9

Issue

7

Pages

669 - 674 (6)

Citation

LI, Z. and BEST, M.C., 2015. Optimization of the input layer structure for feed-forward NARX neural network. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 9 (7), pp. 669 - 674.

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 is an open access article published by WASET and distributed under the terms of the creative commons attribution licence CC BY, https://creativecommons.org/licenses/by/4.0/

Language

en

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