@article{(Open Science Index):https://publications.waset.org/pdf/10001639,
	  title     = {Optimization of the Input Layer Structure for Feed-Forward Narx Neural Networks},
	  author    = {Zongyan Li and  Matt Best},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {9},
	  number    = {7},
	  year      = {2015},
	  pages     = {673 - 678},
	  ee        = {https://publications.waset.org/pdf/10001639},
	  url   	= {https://publications.waset.org/vol/103},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 103, 2015},
	}