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针对Hopfield神经网络的多起点问题，提出了一种新的基于混沌神经网络的盲信号检测算法，实现了二进制移相键控信号盲检测. 据此进一步提出双sigmoid混沌神经网络模型，构造了新的能量函数，且证明了该模型的稳定性，并对网络参数进行配置. 仿真实验表明：混沌神经网络能够避免局部极小点且具备较强的抗噪性能，双sigmoid混沌神经网络则继承了其所有的优点，且其收敛速度更快，仅需更短的接收数据即可到达全局真实平衡点，从而降低了算法的计算复杂度，减少了运行时间.
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关键词:
- 混沌神经网络 /
- 双sigmoid混沌神经网络 /
- 盲检测
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.-
Keywords:
- transiently chaotic Hopfield neural networks /
- double sigmoid transiently chaotic Hopfield neural networks /
- blind signal detection
[1] Zhang Y 2012 Ph. D. Dissertation (Nanjing: Nanjing University of Posts and Telecommunications) (in Chinese) [张昀 2012 博士学位论文 (南京: 南京邮电大学)]
[2] Yang S, Lee C M 2006 IEEE Trans. Circ. Syst. 53 3
[3] Martín-Valdivia M, Ruiz-Sepúlveda A, Triguero-Ruiz F 2000 Neural Networks 13
[4] Chen L N, Aihara K 1997 Physica D 104
[5] Chen L N, Aihara K 1995 Neural Networks 8 6
[6] Chen P F, Chen Z Q, Wu W J 2010 Chin. Phys. B 19 040509
[7] Lou X Y, Cui B T 2008 Acta Phys. Sin. 57 2060 (in Chinese) [楼旭阳, 崔宝同 2008 57 2060]
[8] Park M J, Kwon O M, Park J H, Lee S M, Cha E J 2011 Chin. Phys. B 20 110504
[9] Uykan Z 2013 IEEE Trans. Neural Networks and Learning Systems 24 6
[10] Balavoine A, Romberg J, Rozell C J 2012 IEEE Trans. Circ. Syst. 23 9
[11] Balavoine A, Rozell C J, Romberg J 2011 Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop Sedona AZ, 4–7 Jan. 2011, p431
[12] Zheng P S, Tang W S, Zhang J X 2010 Chin. Phys. B 19 030514
[13] Gong Y R, He D, He C 2012 Acta Phys. Sin. 61 120502 (in Chinese) [宫蕴瑞, 何迪, 何晨 2012 61 120502]
[14] Zhang Y, Zhang Z Y, Yu S J 2012 Acta Phys. Sin. 61 140701 (in Chinese) [张昀, 张志涌, 于舒娟 2012 61 140701]
[15] Zhang Y, Zhang Z Y 2011 Acta Phys. Sin. 60 090703 (in Chinese) [张昀, 张志涌 2011 60 090703]
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[1] Zhang Y 2012 Ph. D. Dissertation (Nanjing: Nanjing University of Posts and Telecommunications) (in Chinese) [张昀 2012 博士学位论文 (南京: 南京邮电大学)]
[2] Yang S, Lee C M 2006 IEEE Trans. Circ. Syst. 53 3
[3] Martín-Valdivia M, Ruiz-Sepúlveda A, Triguero-Ruiz F 2000 Neural Networks 13
[4] Chen L N, Aihara K 1997 Physica D 104
[5] Chen L N, Aihara K 1995 Neural Networks 8 6
[6] Chen P F, Chen Z Q, Wu W J 2010 Chin. Phys. B 19 040509
[7] Lou X Y, Cui B T 2008 Acta Phys. Sin. 57 2060 (in Chinese) [楼旭阳, 崔宝同 2008 57 2060]
[8] Park M J, Kwon O M, Park J H, Lee S M, Cha E J 2011 Chin. Phys. B 20 110504
[9] Uykan Z 2013 IEEE Trans. Neural Networks and Learning Systems 24 6
[10] Balavoine A, Romberg J, Rozell C J 2012 IEEE Trans. Circ. Syst. 23 9
[11] Balavoine A, Rozell C J, Romberg J 2011 Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop Sedona AZ, 4–7 Jan. 2011, p431
[12] Zheng P S, Tang W S, Zhang J X 2010 Chin. Phys. B 19 030514
[13] Gong Y R, He D, He C 2012 Acta Phys. Sin. 61 120502 (in Chinese) [宫蕴瑞, 何迪, 何晨 2012 61 120502]
[14] Zhang Y, Zhang Z Y, Yu S J 2012 Acta Phys. Sin. 61 140701 (in Chinese) [张昀, 张志涌, 于舒娟 2012 61 140701]
[15] Zhang Y, Zhang Z Y 2011 Acta Phys. Sin. 60 090703 (in Chinese) [张昀, 张志涌 2011 60 090703]
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