Abstract
Neural networks are able to learn more patterns with the incremental learning than with the correlative learning. The incremental learning is a method to compose an associate memory using a chaotic neural network. The capacity of the network is found to increase along with its size which is the number of the neurons in the network and to be larger than the one with correlative learning. The appropriate learning parameter is in inverse proportion to the network size. But, in former work, the refractory parameter was fixed to one value, which gives the ability to reinforce memories. In this paper, the capacity of the networks was investigated changing the learning parameter and the refractory parameter. Through the computer simulations, it turned out that the capacity increases over the direct proportion to the network size.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Asakawa, S., Deguchi, T., Ishii, N.: On-Demand Learning in Neural Network. In: Proc. of the ACIS 2nd Intl. Conf. on Software Engineering, Artificial Intelligence, Networking & Parallel/Distributed Computing, pp. 84–89 (2001)
Deguchi, T., Ishii, N.: On Refractory Parameter of Chaotic Neurons in Incremental Learning. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3214, pp. 103–109. Springer, Heidelberg (2004)
Watanabe, M., Aihara, K., Kondo, S.: Automatic learning in chaotic neural networks. In: Proc. of 1994 IEEE Symposium on Emerging Technologies and Factory Automation, pp. 245–248 (1994)
Aihara, K., Tanabe, T., Toyoda, M.: Chaotic neural networks. Phys. Lett. A 144(6,7), 333–340 (1990)
Deguchi, T., Matsuno, K., Ishii, N.: On Capacity of Memory in Chaotic Neural Networks with Incremental Learning. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 919–925. Springer, Heidelberg (2008)
Deguchi, T., Matsuno, K., Kimura, T., Ishii, N.: Error Correction Capability in Chaotic Neural Networks. In: 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, New Jersey, USA, pp. 687–692 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Matsuno, K., Deguchi, T., Ishii, N. (2010). On Influence of Refractory Parameter in Incremental Learning. In: Lee, R. (eds) Computer and Information Science 2010. Studies in Computational Intelligence, vol 317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15405-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-15405-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15404-1
Online ISBN: 978-3-642-15405-8
eBook Packages: EngineeringEngineering (R0)