Incremental Sequential Learning for Fuzzy Neural Networks
HU Rong1, 2, XU Wei-Hong1, 3, GAN Lan1, 4
1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
2.Aviation Institute of Electrical and Electronic Engineering, Changsha Aeronautical Vocational and Technical College, Changsha 410014
3.Computer and Communication Engineering Institute, Changsha University of Science and Technology, Changsha 410015
4.School of Information Engineering, East China Jiaotong University, Nanchang 330013
To gain a fast, accurate and parsimonious fuzzy neural network, an effective incremental sequential learning algorithm for parsimonious fuzzy neural networks (ISL-FNN)is proposed. The pruning strategy is introduced into the generation of neurons. The error reduction ratio is used to define the influence of input data on the output and the influence is utilized for the generation of neurons. In the parameter learning phase, all the free parameters of hidden units, including the newly created and the originally existing, are updated by the extended Kalman filter method. The performance of ISL-FNN is compared with several existing algorithms on some benchmark problems. Result indicates that ISL-FNN produces similar or even better accuracies with less number of rules.
[1] Mitra S, Hayashi Y. Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework. IEEE Trans on Neural Networks, 2000, 11 (3): 748-768
[2] Lee C W, Shin Y C. Construction of Fuzzy Systems Using Least-Squares Method and Genetic Algorithm. Fuzzy Sets and Systems, 2003, 137(3): 297-323
[3] Leng G, McGinnity T M, Prasad G. An Approach for On-line Extraction of Fuzzy Rules Using a Self-Organising Fuzzy Neural Network. Fuzzy Sets and Systems, 2005, 150(2): 211-243
[4] Platt J. A Resource-Allocating Network for Function Interpolation. Neural Computation, 1991, 3(2): 213-225
[5] Kadirkamanathan V, Niranjan M. A Function Estimation Approach to Sequential Learning with Neural Networks. Neural Computation, 1993, 5(6): 954-975
[6] Chen S, Cowan C F N, Grant P M. Orthogonal Least Squares Learning Algorithm for Radial Basis Function Network. IEEE Trans on Neural Networks, 1991, 2(2): 302-309
[7] Wu Shiqian, Er Meng J. Dynamic Fuzzy Neural Networks-A Novel Approach to Function Approximation. IEEE Trans on Systems, Man and Cybernetics, 2000, 30 (2): 358-364
[8] Wu Shiqian, Er Meng J, Gao Yang. A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks. IEEE Trans on Fuzzy Systems, 2001, 9(4): 578-593
[9] Wang Ning, Er Meng J, Meng Xianyao. A Fast and Accurate Online Self-Organizing Scheme for Parsimonious Fuzzy Neural Networks. Neurocomputing, 2009, 72 (16/17/18): 3818-3829
[10] Pouzols F M, Lendasse A. Evolving Fuzzy Optimally Pruned Extreme Learning Machine for Regression Problems. Evolving Systems, 2010, 1(1): 43-58
[11] Hu Rong, Xu Weihong, Kuang Fangjun. An Improved Incremental Singular Value Decomposition. International Journal of Advancements in Computing Technology, 2012, 4(2): 95-102
[12] Rong Haijun, Sundararajan N, Huang Guangbin, et al. Sequential Adaptive Fuzzy Inference System (SAFIS) for Nonlinear System Identification and Prediction. Fuzzy Sets and Systems, 2006, 157 (9): 1260-1275
[13] Liang N, Huang G, Saratchandran P, et al. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. IEEE Trans on Neural Networks, 2006, 17(6): 1411-1423
2013, Vol. 26 
