Abstract
A novel hybrid cluster-based self-organizing neuro-fuzzy system (HC-SONFS) is proposed for dynamic function approximation and prediction. With the mechanism of self-organization, fuzzy rules are generated in the form of clusters using the proposed self-organization method to achieve compact and sufficient system structure if the current structure of knowledge base is insufficient to satisfy the required performance. A hybrid learning algorithm combining the well-known random optimization (RO) and the least square estimation (LSE) is use for fast learning. An example of chaos time series for system identification and prediction is illustrated. Compared to other approaches, excellent performance of the proposed HC-SONFS is observed.
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
Li, C., Lee, C.Y.: Self-organizing neuro-fuzzy system for control of unknown plants. IEEE Trans. Fuzzy Syst. 11(1), 135–150 (2003)
Li, C., Priemer, R., Cheng, K.-H.: Optimization by Random Search with Jumps. International Journal for Numerical Methods in Engineering 60, 1301–1315 (2004)
Wu, S., Er, M.J.: Dynamic Fuzzy Neural Networks-A Novel Approach to Function Approximation. IEEE Trans. Syst. Man. Cybern., Part B: Cybernetics 30(2), 358–364 (2000)
Cho, K.B., Wang, B.H.: Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction. Fuzzy Sets and Systems 83, 325–339 (1996)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basic function network. IEEE Trans. Neural Networks 2, 302–309 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Cheng, KH., Chen, CM., Chen, JL. (2005). Cluster-Based Self-organizing Neuro-fuzzy System with Hybrid Learning Approach for Function Approximation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_150
Download citation
DOI: https://doi.org/10.1007/11539902_150
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)