A genetic algorithm based variable structure Neural Network

This paper presents a neural network model with a variable structure, which is trained by genetic algorithm (GA). The proposed neural network consists of a Neural Network with a Node-to-Node Relationship (N/sup 4/R) and a Network Switch Controller (NSC). In the N/sup 4/R, a modified neuron model with two activation functions in the hidden layer, and switches in its links are introduced. The NSC controls the switches in the N/sup 4/R. The proposed neural network can model different input patterns with variable network structures. The proposed neural network provides better result and learning ability than traditional feed forward neural networks. Two application examples on XOR problem and hand-written pattern recognition are given to illustrate the merits of the proposed network.

[1]  Hak-Keung Lam,et al.  On interpretation of graffiti digits and characters for eBooks: neural-fuzzy network and genetic algorithm approach , 2004, IEEE Transactions on Industrial Electronics.

[2]  F.H.F. Leung,et al.  Learning of neural network parameters using a fuzzy genetic algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Hak-Keung Lam,et al.  Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm , 2004, IEEE Transactions on Industrial Electronics.

[4]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[5]  F.H.F. Leung,et al.  A novel GA-based neural network for short-term load forecasting , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[6]  H. Youlal,et al.  Fuzzy dynamic path planning using genetic algorithms , 2000 .

[7]  Hak-Keung Lam,et al.  Design and stability analysis of fuzzy model-based nonlinear controller for nonlinear systems using genetic algorithm , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  F.H.F. Leung,et al.  On interpretation of graffiti digits and commands for eBooks: neural fuzzy network and genetic algorithm approach , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).