optimization-of-the-input-layer-structure-for-feed-forward-narx-neural-networks.pdf (247.22 kB)
Optimization of the input layer structure for feed-forward NARX neural network
journal contributionposted on 2016-09-22, 12:27 authored 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.
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.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inInternational Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
Pages669 - 674 (6)
CitationLI, 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.
PublisherWorld Academy of Science, Engineering and Technology (WASET)
- VoR (Version of Record)
Publisher statementThis 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/
NotesThis 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/