Identification of nonlinear system based on improved neural network sequential learning algorithm

Neural network is one of the most effective tools in nonlinear control system design. In practical online application, sequential learning algorithms are preferred over batch learning algorithms because they do not require retraining whenever a new data is trained. However, the existing sequential learning algorithms only utilize the current instant estimation of the nonlinear system for constructing the network structure. Therefore they do not characterize temporal variability well. To overcome this problem, the multi-step-ahead predictor of the nonlinear system is introduced to the growing and pruning network for constructing network structure. Furthermore, a sliding window model is used to prevent the network from fitting the noise if there is noise in the input data. And in order to reduce the computation load, the winner neuron strategy is utilized to update the parameters of the neural network using extended Kalman filter. Experimental results show that the proposed algorithm can obtain more compact network along with smaller errors in mean square sense than other typical sequential learning algorithms.

[1]  Vijanth S. Asirvadam,et al.  Fast and efficient sequential learning algorithms using direct-link RBF networks , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[2]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[3]  G.W. Irwin,et al.  Fast sequential learning methods on RBF-network using decomposed training algorithms , 2004, Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004..

[4]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[5]  Mahesan Niranjan,et al.  Pruning with Replacement on Limited Resource Allocating Networks by F-Projections , 1996, Neural Computation.

[6]  Esther Levin.  Hidden control neural architecture modeling of nonlinear time varying systems and its applications , 1993, IEEE Trans. Neural Networks.

[7]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.

[8]  Narasimhan Sundararajan,et al.  A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation , 2005, IEEE Transactions on Neural Networks.

[9]  Y Lu,et al.  A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks , 1997, Neural Computation.

[10]  Yan Li,et al.  Analysis of minimal radial basis function network algorithm for real-time identification of nonlinear dynamic systems , 2000 .

[11]  Narasimhan Sundararajan,et al.  An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).