Abstract
In this article, we propose a method to improve the transiently chaotic neural network by introducing several time-dependent parameters. With this method, the network processes by starting at rich chaotic dynamics, and reaches stable state for all neurons rapidly after the last bifurcation. This enables the network to have rich search ability at the beginning, and use less CPU time to reach a stable state. The simulation results on the N-queen problem confirm that this method is effective to improve TCNN in terms of both the solution quality and convergence speed.
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© 2004 Springer-Verlag Berlin Heidelberg
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Xu, X., Wang, J., Tang, Z., Li, X.C.Y., Xia, G. (2004). A Method to Improve the Transiently Chaotic Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_67
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DOI: https://doi.org/10.1007/978-3-540-28647-9_67
Publisher Name: Springer, Berlin, Heidelberg
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