An enhanced online self-organizing fuzzy neural network

An Enhanced Online Self-organizing Fuzzy Neural Network (EOS-FNN) is proposed in this paper. The proposed algorithm can improve computational efficiency while achieving comparable performance and accuracy compared to other methods. The proposed EOS-FNN starts with an empty rule set and automatically generates fuzzy rules according to the proposed criteria during the learning process. All the parameters of the fuzzy rules are updated by the Extended Kalman Filter (EKF) method. Nonlinear time-series prediction processes are used to evaluate the performance of the proposed EOS-FNN algorithm with a comparison to other popular algorithms including DFNN, GDFNN and FAOS-PFNN. Simulation results have shown that the proposed algorithm reduces computation time while achieving comparable accuracy.

[1]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[2]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[3]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[4]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

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

[6]  Meng Joo Er,et al.  Dynamic fuzzy neural networks-a novel approach to function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[7]  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..

[8]  T. Martin McGinnity,et al.  An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network , 2005, Fuzzy Sets Syst..

[9]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[10]  Uzay Kaymak,et al.  Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system , 2006, Comput. Geosci..

[11]  Y. Varol,et al.  Prediction of flow fields and temperature distributions due to natural convection in a triangular enclosure using Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) , 2007 .

[12]  Meng Joo Er,et al.  An Online Self-constructing Fuzzy Neural Network with Restrictive Growth , 2009, ISNN.

[13]  Meng Joo Er,et al.  A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks , 2009, Neurocomputing.