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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22533

Title: Optimization of the input layer structure for feed-forward NARX neural network
Authors: Li, Zongyan
Best, Matt C.
Keywords: Correlation analysis
Neural network
Issue Date: 2015
Publisher: World Academy of Science, Engineering and Technology (WASET)
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.
Abstract: 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.
Description: 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/
Sponsor: 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.
Version: Published
URI: https://dspace.lboro.ac.uk/2134/22533
Publisher Link: http://www.waset.org/Publications/?path=Publications
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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