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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/17385

Title: Optimisation of the input layer structure for feed-forward NARX neural network
Authors: Li, Zongyan
Best, Matt C.
Keywords: Optimisation
Correlation analysis
Neural network
Issue Date: 2015
Publisher: © World Academy of Science Engineering and Technology (WASET)
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.
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 paper was presented at: ICMICE 2015: 17th International Conference on Modelling, Identification and Control Engineering, 9th-10th July 2015, Prague, Czech Republic.
Version: Published
URI: https://dspace.lboro.ac.uk/2134/17385
Publisher Link: https://www.waset.org/conference/2015/07/prague/ICMICE
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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