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.
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 paper was presented at: ICMICE 2015: 17th International Conference on Modelling, Identification and Control Engineering, 9th-10th July 2015, Prague, Czech Republic.