Novel improved blind detection algorithms based on chaotic neural networks

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.