Abstract
In this paper we apply the transiently chaotic Hopfield neural networks (TCHNN) to the blind signal detection algorithm with BPSK signals and solve multi-start problem of Hopfield neural networks (HNN). And in this paper we propose an improved algorithm of double sigmoid transiently chaotic Hopfield neural networks (DS-TCHNN) on the basis of TCHNN, construct a new energy function of DS-TCHNN, and prove the stability of DS-TCHNN in asynchronous update mode and synchronous update mode. Simulation results show that TCHNN can skip local minima and has better anti-noise performance than HNN. While, DS-TCHNN inherits all the advantages of TCHNN and its speed of convergence is fast. Besides, DS-TCHNN needs shorter data to reach a global true equilibrium point so that the computational complexity is reduced and the running time is shortened.
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