Optimization of the Input Layer Structure for Feed-Forward Narx Neural Networks
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.
[1] D. Montana and L. Davis, “Training feedforward neural networks using
genetic algorithms”. Proc.1989 International Joint Conf. Artificial
Intelligence.
[2] M. Khashei and M. Bijari, “An artificial neural network (p, d,q) model
for timeseries forecasting”. Expert Systems with Applications Vol. 37
,2010, pp. 479–489
[3] B. Pradhan and S. Lee and M. F. Buchroithner, “A GIS-based backpropagation
neural network model and its cross-application and
validation for landslide susceptibility analyses”. Computers,
Environment and Urban Systems, Vol 34, 2010, pp. 216-235
[4] M.A. Mohandes and S. Rehman and T.O. Halawani, “A neural networks
approach for wind speed prediction”. Renewable Energy, Vol. 13,No. 3,
1998, pp.345-354
[5] L. Zhang and F. Tian and S. Liu etc, “Chaos based neural network
optimization for concentration estimation of indoor air contaminants by
an electronic nose”. Sensors and Actuators A, Vol. 189, 2013, pp. 161-
167.
[6] P. G. Benardos, and G. C. Vosniakos, “Prediction of surface roughness
in CNC face milling using neural networks and Taguchi’s design of
experiments” Robotics and Computer Integrated Manufacturing, Vol.
18, 2002, pp. 43–354. [7] L. Ma, and K. Khorasani, “A new strategy for adaptively constructing
multilayer feed-forward neural networks”. Neurocomputing, 51, 2003,
pp. 361–385.
[8] J. P Ross, “Taguchi techniques for quality engineering”. New York:
McGraw-Hill, 1996.
[9] S. D. Balkin and J. K. Ord, “Automatic neural network modeling for
univariate time series”. International Journal of Forecasting, Vol. 16,
2000, pp. 509–515
[10] M. M. Islam and K. Murase, “A new algorithm to design compact two
hidden layer artificial neural networks”. Neural Networks, 14, 2001,
pp.1265–1278.
[11] X. Jiang and A.H.K.S. Wah, “Constructing and training feed-forward
neural networks for pattern classification”. Pattern Recognition Vol. 36,
2003, pp.853–867.
[12] P. G. Benardos and G. C. Vosniakos, “Optimizing feed-forward artificial
neural network architecture”. Engineering Applications of Artificial
Intelligence, 20, 2007, pp. 365–382.
[13] G. Zhang and B. E. Patuwo and M. Y. Hu, “Forecasting with artificial
neural networks: The state of the art”, International Journal of
Forecasting, Vol. 14, Issue 1, March, 1998, pp 35–62.
[14] D. Marquardt, “An Algorithm for Least-Squares Estimation of
Nonlinear Parameters”. SIAM Journal on Applied Mathematics, Vol. 11,
No. 2, June 1963, pp. 431–441.
[15] M. T. Hagan and M. Menhaj, “Training feed-forward networks with the
Marquardt algorithm”, IEEE Transactions on Neural Networks, Vol. 5,
No. 6,1994, pp. 989–993.
[16] D. Whitley and T. Starkweather and C. Bogart, “Genetic algorithms and
neural networks: optimizing connections and connectivity”. Parallel
Computing Vol.14, 1990, pp. 347-361
[1] D. Montana and L. Davis, “Training feedforward neural networks using
genetic algorithms”. Proc.1989 International Joint Conf. Artificial
Intelligence.
[2] M. Khashei and M. Bijari, “An artificial neural network (p, d,q) model
for timeseries forecasting”. Expert Systems with Applications Vol. 37
,2010, pp. 479–489
[3] B. Pradhan and S. Lee and M. F. Buchroithner, “A GIS-based backpropagation
neural network model and its cross-application and
validation for landslide susceptibility analyses”. Computers,
Environment and Urban Systems, Vol 34, 2010, pp. 216-235
[4] M.A. Mohandes and S. Rehman and T.O. Halawani, “A neural networks
approach for wind speed prediction”. Renewable Energy, Vol. 13,No. 3,
1998, pp.345-354
[5] L. Zhang and F. Tian and S. Liu etc, “Chaos based neural network
optimization for concentration estimation of indoor air contaminants by
an electronic nose”. Sensors and Actuators A, Vol. 189, 2013, pp. 161-
167.
[6] P. G. Benardos, and G. C. Vosniakos, “Prediction of surface roughness
in CNC face milling using neural networks and Taguchi’s design of
experiments” Robotics and Computer Integrated Manufacturing, Vol.
18, 2002, pp. 43–354. [7] L. Ma, and K. Khorasani, “A new strategy for adaptively constructing
multilayer feed-forward neural networks”. Neurocomputing, 51, 2003,
pp. 361–385.
[8] J. P Ross, “Taguchi techniques for quality engineering”. New York:
McGraw-Hill, 1996.
[9] S. D. Balkin and J. K. Ord, “Automatic neural network modeling for
univariate time series”. International Journal of Forecasting, Vol. 16,
2000, pp. 509–515
[10] M. M. Islam and K. Murase, “A new algorithm to design compact two
hidden layer artificial neural networks”. Neural Networks, 14, 2001,
pp.1265–1278.
[11] X. Jiang and A.H.K.S. Wah, “Constructing and training feed-forward
neural networks for pattern classification”. Pattern Recognition Vol. 36,
2003, pp.853–867.
[12] P. G. Benardos and G. C. Vosniakos, “Optimizing feed-forward artificial
neural network architecture”. Engineering Applications of Artificial
Intelligence, 20, 2007, pp. 365–382.
[13] G. Zhang and B. E. Patuwo and M. Y. Hu, “Forecasting with artificial
neural networks: The state of the art”, International Journal of
Forecasting, Vol. 14, Issue 1, March, 1998, pp 35–62.
[14] D. Marquardt, “An Algorithm for Least-Squares Estimation of
Nonlinear Parameters”. SIAM Journal on Applied Mathematics, Vol. 11,
No. 2, June 1963, pp. 431–441.
[15] M. T. Hagan and M. Menhaj, “Training feed-forward networks with the
Marquardt algorithm”, IEEE Transactions on Neural Networks, Vol. 5,
No. 6,1994, pp. 989–993.
[16] D. Whitley and T. Starkweather and C. Bogart, “Genetic algorithms and
neural networks: optimizing connections and connectivity”. Parallel
Computing Vol.14, 1990, pp. 347-361
@article{"International Journal of Electrical, Electronic and Communication Sciences:70155", author = "Zongyan Li and Matt Best", title = "Optimization of the Input Layer Structure for Feed-Forward Narx Neural Networks", 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.", keywords = "Correlation analysis, F-ratio, Levenberg-Marquardt,
MSE, NARX, neural network, optimisation.", volume = "9", number = "7", pages = "673-6", }