A Method to Improve the Transiently Chaotic Neural Network

摘要

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 W-queen problem confirm that this method is effective to improve TCNN in terms of both the solution quality and convergence speed.