Abstract
A union-based rule-antecedent fuzzy neural networks (URFNN), which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The URFNN allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the URFNN, we consider the union-based logic processor (ULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, genetic algorithm (GA) constructs a Boolean skeleton of URFNN, while gradient-based learning refines the binary connections of GA-optimized URFNN for further improvement of the performance index. A cart-pole system is considered to verify the effectiveness of the proposed method.
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
Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965)
King, P.J., Mamdani, E.H.: The application of fuzzy control systems to industrial processes. Automatica 13(3), 235–242 (1977)
Pal, S.K., King, R.A., Hashim, A.A.: Image description and primitive extraction using fuzzy set. IEEE Trans. Syst. Man and Cybern. SMC-13, 94–100 (1983)
Karr, C.L.: Design of an adaptive fuzzy logic controller using a genetic algorithm. In: Proc. Int. Conf. on Genetic Algorithms, pp. 450–457 (1991)
Homaifar, A., McCormick, E.: Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Systems 3(2), 129–139 (1995)
Pedrycz, W., Reformat, M., Han, C.W.: Cascade architectures of fuzzy neural networks. Fuzzy Optimization and Decision Making 3(1), 5–37 (2004)
Pedrycz, W.: Fuzzy Neural Networks and Neurocomputations. Fuzzy Sets and Systems 56, 1–28 (1993)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Liu, B.D., Chen, C.Y., Tsao, J.Y.: Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms. IEEE Trans. Syst. Man and Cybern.-B 31(1), 32–53 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, CW. (2008). Evolutionary Optimization of Union-Based Rule-Antecedent Fuzzy Neural Networks and Its Applications. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-88906-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
eBook Packages: Computer ScienceComputer Science (R0)