Abstract
Choquet Integral Agent Network (CHIAN) is proposed as a method realizing flexible information fusion which is constructed by using fuzzy measure and Choquet integrals. In case of multi-layered network structure, CHIAN can employ back-propagation algorithms-like concept for learning process. However, the back-propagation methods have some limitations such as trapping at local minima and network paralysis. Due to genetic algorithms (GA) mechanism, it has the characteristics of hill climbing, and thus can overcome the difficulty of trapping at local minima; consequently it might reduce network paralysis. This paper aims at proposing to tune CHIAN learning parameters, i.e., learning rate and momentum coefficient by genetic algorithms for improving CHIAN as classifier, pattern recognition, and information fusion. The results show that the network evolved GA requires fewer training cycles than the network which the learning parameters are intuitively given.
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
The Berkeley Initiative in Soft Computing website, http://www-bisc.cs.berkeley.edu/BISCProgram/History.htm
Takahashi, H., Agui, T., Nagahashi, H.: Designing Adaptive Neural Network Architectures and their Learning Parameters using Genetic Algorithms. In: Ruck, D.W. (ed.) Science of Artificial Neural Networks II. Proceeding of SPIE, vol. 1966, pp. 208–215 (1993)
Konar, A.: Behavioral Synergism of Soft Computing Tools. In: Computational Intelligent: Principles, Techniques and Applications. Springer, Heidelberg (2005)
Konar, A.: Genetic Algorithms. In: Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain. CRC, Boca Raton (1999)
Nakamura, K.: A Scheme for information fusion by Choquet Integral Agent Networks. In: Eighth IFSA Congress, pp. 954–958 (1999)
Nakamura, K.: Towards Inverse Problems of Choquet Integral Agent Networks as Information Fusion Mechanisms. In: Proceeding of the 6th International Symposium on Advanced Intelligent Systems, pp. 675–678 (2005)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, Irvine, CA: University of California, Department of Information and Computer Science (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Baudat, G., Anour, F.: Generalized Discriminant Analysis using a Kernel Approach 12(10), 2385–2404 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Handri, S., Nakamura, K. (2009). Adaptation of Learning Parameters for Choquet Integral Agent Network by Using Genetic Algorithms. In: Avineri, E., Köppen, M., Dahal, K., Sunitiyoso, Y., Roy, R. (eds) Applications of Soft Computing. Advances in Soft Computing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88079-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-88079-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88078-3
Online ISBN: 978-3-540-88079-0
eBook Packages: EngineeringEngineering (R0)