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. In former work, the capacity was over the direct proportion to the network size with suitable pairs of the refractory parameter and the learning parameter. In this paper, the refractory parameter and the learning parameter are investigated through the computer simulations changing these parameters. Through the computer simulations, it turns out that the appropriate parameters lie near the origin with some relation between them.
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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, Part II. 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)
Matsuno, K., Deguchi, T., Ishii, N.: On Influence of Refractory Parameter in Incremental Learning. In: Lee, R. (ed.) Computer and Information Science 2010. SCI, vol. 317, pp. 13–21. Springer, Heidelberg (2010)
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Deguchi, T., Fukuta, J., Ishii, N. (2013). On Appropriate Refractoriness and Weight Increment in Incremental Learning. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_1
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DOI: https://doi.org/10.1007/978-3-642-37213-1_1
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