Research on an online self-organizing radial basis function neural network

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms.

[1]  Lajos Hanzo,et al.  Symmetric RBF Classifier for Nonlinear Detection in Multiple-Antenna-Aided Systems , 2008, IEEE Transactions on Neural Networks.

[2]  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).

[3]  Juan Ignacio Mulero Martínez.  Boundedness of the nominal coefficients in Gaussian RBF neural networks , 2007, Neurocomputing.

[4]  Andreas Wichert,et al.  Flexible kernels for RBF networks , 2006, Neurocomputing.

[5]  Héctor Pomares,et al.  Time series analysis using normalized PG-RBF network with regression weights , 2002, Neurocomputing.

[6]  Dingli Yu,et al.  Adaptive RBF network for parameter estimation and stable air-fuel ratio control , 2008, Neural Networks.

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

[8]  Meng Joo Er,et al.  A fast learning algorithm for parsimonious fuzzy neural systems , 1999 .

[9]  Manjaree Pandit,et al.  Corrective action planning using RBF neural network , 2007, Appl. Soft Comput..

[10]  Guanrong Chen,et al.  Energy Function and Energy Evolution on Neuronal Populations , 2008, IEEE Transactions on Neural Networks.

[11]  T. Tran-Cong,et al.  APPROXIMATION OF FUNCTION AND ITS DERIVATIVES USING RADIAL BASIS FUNCTION NETWORKS , 2003 .

[12]  Nam Mai-Duy,et al.  Approximation of function and its derivatives using radial basis function networks , 2003 .

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

[14]  Yanchun Liang,et al.  Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting , 2005, Neural Computing & Applications.

[15]  Mahantapas Kundu,et al.  Face recognition using point symmetry distance-based RBF network , 2007, Appl. Soft Comput..

[16]  Noriaki Suetake,et al.  An RBFN-Wiener hybrid filter using higher order signal statistics , 2007, Appl. Soft Comput..

[17]  Meng Joo Er,et al.  A fast learning algorithm for parsimonious fuzzy neural systems , 1999, 1999 European Control Conference (ECC).

[18]  Nicolaos B. Karayiannis,et al.  Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques , 1997, IEEE Trans. Neural Networks.

[19]  Meng Joo Er,et al.  A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks , 2001, IEEE Trans. Fuzzy Syst..

[20]  Héctor Pomares,et al.  Studying possibility in a clustering algorithm for RBFNN design for function approximation , 2007, Neural Computing and Applications.

[21]  Derong Liu,et al.  Wavelet Basis Function Neural Networks for Sequential Learning , 2008, IEEE Transactions on Neural Networks.

[22]  Tong Zhao,et al.  RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems , 2008, Neural Computing and Applications.

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

[24]  Ignacio Rojas,et al.  SSA, SVD, QR-cp, and RBF Model Reduction , 2002, ICANN.

[25]  P. Torkzadeh,et al.  Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network , 2008 .