Abstract
In this paper, we present an improved learning scheme for extracting T-S fuzzy rules from data samples, whereby a neuro-fuzzy architecture implements the T-S fuzzy system using ellipsoidal basis functions. The salient characteristics of this approach are as follows: 1) A novel structure learning algorithm incorporating a pruning strategy into new growth criteria is developed. 2) Compact fuzzy rules can be extracted from training data. 3) The linear least squares (LLS) method is employed to update consequent parameters, and thereby contributing to high approximation accuracy. Simulation studies and comprehensive comparisons with other well-known algorithms demonstrate the effective and superior performance of our proposed scheme in terms of compact structure and promising accuracy.
This work is supported by the National Natural Science Foundation of China (51009017), Applied Basic Research Funds from Ministry of Transport of P. R. China (2012-329-225-060), China Postdoctoral Science Foundation (2012M520629), and Fundamental Research Funds for the Central Universities of China (2009QN025, 2011JC002 and 3132013025).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, L.X.: Adaptive Fuzzy System and Control: Design and Stability Analysis. Prentice-Hall, Englewood Cliffs (1994)
Jang, J.-S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. Syst., Man, Cybern. 22, 665–684 (1993)
Lin, C.T.: A Neural Fuzzy Control System with Structure and Parameter Learning. Fuzzy Sets Syst. 70, 183–212 (1995)
Cho, K.B., Wang, B.H.: Radial Basis Function Based Adaptive Fuzzy Systems and Their Applications to System Identification and Prediction. Fuzzy Sets Syst. 83, 325–339 (1996)
Juang, C.F., Lin, C.T.: An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications. IEEE Trans. Fuzzy Syst. 6, 12–32 (1998)
Platt, J.: A Resource-Allocating Network for Function Interpolation. Neural Comput. 5, 213–225 (1991)
Kadirkamannathan, V., Niranjan, M.: A Function Estimation Approach to Sequential Learning with Neural Networks. Neural Comput. 5, 954–975 (1993)
Yingwei, L., Sundararajan, N., Saratchandran, P.: Performance Evaluation of a Sequential Minimal Radial Basis Function (RBF) Neural Networks Learning Algorithm. IEEE Trans. Neural Netw. 9, 308–318 (1998)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Network. IEEE Trans. Neural Netw. 2, 1411–1423 (1996)
Wu, S.-Q., Er, M.J.: Dynamic Fuzzy Neural Networks-A Novel Approach to Function Approximation. IEEE Trans. Syst., Man, Cybern., B, Cybern. 30, 358–564 (2000)
Wu, S.-Q., Er, M.J., Gao, Y.: A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks. IEEE Trans. Fuzzy Syst. 9, 578–594 (2001)
Wang, N., Er, M.J., Meng, X.Y.: A Fast and Accurate Online Self-Organizing Scheme for Parsimonious Fuzzy Neural Networks. Neurocomput. 72, 3818–3829 (2009)
Wang, N.: A Generalized Ellipsoidal Basis Function Based Online Self-Constructing Fuzzy Neural Network. Neural Process. Lett. 34, 13–37 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, N., Wang, X., Tan, Y., Shao, P., Han, M. (2013). An Improved Learning Scheme for Extracting T-S Fuzzy Rules from Data Samples. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-39068-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
eBook Packages: Computer ScienceComputer Science (R0)