Abstract
In this paper, we propose a novel associative chaotic neural network (NACNN) via exponential decay effect. We replace historic spatio-temporal effect on neural network with new exponential decay parameters, which is more close to the facts. As we know, historic effect on our memory always decreases at exponential level with the time increasing. The proposed model can realize one-to-many associations perfectly. The effectiveness of our scheme is illustrated by a series of computer simulations.
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Duan, S., Wang, L. (2005). Associative Chaotic Neural Network via Exponential Decay Spatio-temporal Effect. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_78
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DOI: https://doi.org/10.1007/11427391_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
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