Abstract
A new complex neuro-fuzzy self-learning approach to the problem of function approximation is proposed, where complex fuzzy sets are used to design a complex neuro-fuzzy system as the function approximator. Particle swarm optimization (PSO) algorithm and recursive least square estimator (RLSE) algorithm are used in hybrid way to adjust the free parameters of the proposed complex neuro-fuzzy systems (CNFS). The hybrid PSO-RLSE learning method is used for the CNFS parameters to converge efficiently and quickly to optimal or near-optimal solution. From the experimental results, the proposed CNFS shows better performance than the traditional neuro-fuzzy system (NFS) that is designed with regular fuzzy sets. Moreover, the PSO-RLSE hybrid learning method for the CNFS improves the rate of learning convergence, and shows better performance in accuracy. Three benchmark functions are used. With the performance comparisons shown in the paper, excellent performance by the proposed approach has been 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
Juang, C.F., Lin, C.T.: An online self-constructing neural fuzzy inference network and its applications. IEEE Transactions on Fuzzy Systems 6, 12–32 (1998)
Paul, S., Kumar, S.: Subsethood-product fuzzy neural inference system. IEEE Transactions on Neural Networks 13, 578–599 (2002)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feed forward networks are universal approximators. Neural networks 2, 359–366 (1989)
Wang, L.X., Mendel, J.M.: Fuzzy basis functions, universal approximation, and orthogonalleast-squares learning. IEEE Transactions on Neural Networks 3, 807–814 (1992)
Kandel, A., Ramot, D., Milo, R., Friedman, M.: Complex Fuzzy Sets. IEEE Transactions on Fuzzy Systems 10, 171–186 (2002)
Dick, S.: Toward complex fuzzy logic. IEEE Transactions on Fuzzy Systems 13, 405–414 (2005)
Ramot, D., Friedman, M., Langholz, G., Kandel, A.: Complex fuzzy logic. IEEE Transactions on Fuzzy Systems 11, 450–461 (2003)
Farag, W.A., Quintana, V.H., Lambert-Torres, G.: A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems. IEEE Transactions on Neural Networks 9, 756–767 (1998)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics 15, 116–132 (1985)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks (1995)
Hsia, T.C.: System identification: Least-squares methods. D. C. Heath and Company (1977)
Wu, J.M., Lin, Z.H., Hus, P.H.: Function approximation using generalized adalines. IEEE Transactions on Neural Networks 17, 541–558 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Chiang, TW. (2010). Complex Neuro-Fuzzy Self-learning Approach to Function Approximation. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12101-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-12101-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12100-5
Online ISBN: 978-3-642-12101-2
eBook Packages: Computer ScienceComputer Science (R0)