Open AccessNovel improved blind detection algorithms based on chaotic neural networks
Author(s) -
Shujuan Yu,
Huan Ru-Song,
Yun Zhang,
Di Feng
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.060701
Subject(s) - computer science , sigmoid function , artificial neural network , chaotic , maxima and minima , hopfield network , algorithm , asynchronous communication , convergence (economics) , noise (video) , construct (python library) , function (biology) , artificial intelligence , mathematics , telecommunications , mathematical analysis , evolutionary biology , economics , image (mathematics) , biology , programming language , economic growth
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.