A method to improve the transiently chaotic neural network

In this article, we propose a method for improving the transiently chaotic neural network (TCNN) by introducing several time-dependent parameters. This method allows the network to have rich chaotic dynamics in its initial stage and to reach a state in which all neurons are stable soon after the last bifurcation. This enables the network to have rich search ability initially and to use less CPU time to reach a stable state. The simulation results on the N-queen problem confirm that this method effectively improves both the solution quality and convergence speed of TCNN.