Abstract
The paper addresses the problem of online adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both antecedent and consequent parts of fuzzy rules is proposed. The algorithm is derived from the well-known Marquardt procedure and uses approximation of the Hessian matrix. A characteristic feature of the proposed algorithm is that it does not require time-consuming matrix operations. Simulation results illustrate application to adaptive identification of a nonlinear plant and nonlinear time series prediction.
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.
Similar content being viewed by others
References
Rumelhart, D.E., Hinton, G.R., Williams, R.J.: Learning Internal Representation by Error Propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.): Parallel Distributed Processing, Vol. 1. MIT Press, Cambridge, MA (1986) 318–364
Takagi, T., Sugeno, M.: Derivation of fuzzy control rules from human operator’s control actions. Proc. of the IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis (1983) 55–60
Marquardt, D.W.: An Algorithm for Least Squares Estimation of Non-Linear Parameters. SIAM J. Appl. Math. 11 (1963) 431–441
Rojas, R.: Neural Networks. A Systematic Introduction. Springer-Verlag, Berlin Heidelberg New York (1996)
Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. on Neural Networks 1 (1990) 4–27
Lin, C.-J.: SISO Nonlinear System Identification Using a Fuzzy-Neural Hybrid System. Int. J. Neural Systems, Vol. 8 (1997) 325–337
Mackey, M. C., Glass, L.: Oscillation and chaos in physiological control systems. Science, 197 (1977) 287–289.
Jang, J.-S. R.: ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. on Systems, Man, and Cybernetics, Vol. 23 (1993) 665–685
Nauck, D., Kruse, R.: A Neuro-Fuzzy Approach to Obtain Interpretable Fuzzy Systems for Function Approximation. Proc. of the IEEE Int. Conf. on Fuzzy Systems (1998) 1106–1111
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bodyanskiy, Y., Kolodyazhniy, V., Stephan, A. (2001). An Adaptive Learning Algorithm for a Neuro-fuzzy Network. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_11
Download citation
DOI: https://doi.org/10.1007/3-540-45493-4_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42732-2
Online ISBN: 978-3-540-45493-9
eBook Packages: Springer Book Archive