World Academy of Science, Engineering and Technology (WASET)
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.
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 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/
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.