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Blind detection algorithm of complex multi-valued discrete Hopfield network

Zhang Yun Zhang Zhi-Yong

Blind detection algorithm of complex multi-valued discrete Hopfield network

Zhang Yun, Zhang Zhi-Yong
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  • A novel complex multi-valued discrete Hopfield neural network (CMDHNN) is proposed in this paper. A multi-valued discrete activation function and a new energy function for CMDHNN are constructed. The stabilities for multi-valued CMDHNN with asynchronous and synchronous operating modes are also analyzed seperately. The special energy functions own the ability to describe the dynamic characteristics of CMDHNN which the energy functions of existing references cannot explain. Meantime, these energy functions can make the true source signal vector correspond to the minimum point of the energy function of CMDHNN. Furthermore, to verify effectiveness of CMDHNN, the weighted matrix of CMDHNN is constructed by the specific cost function for the blind detection of signals. Simulation results show that the proposed CMDHNN can be used to blindly detect the dense MQAM constellation signals with shorter received signals and the global minimal value of the CMDHNN energy function is verified.
    • Funds:
    [1]

    Gao H S, Zhang J 2008 Fourth International Conference on Natural Computation, Jinan, China, October 18—20,2008, 560

    [2]

    Cui B T, Chen J, Lou X Y 2008 Chin. Phys. B 17 1670

    [3]

    Qiu F, Cui B T, Ji Y 2009 Chin. Phys. B 18 5203

    [4]

    Xiong T, Zhang B L 2005 Acta Phys. Sin. 54 2435(in Chinese)[熊 涛、张便利 2005 物理学报 54 2435]

    [5]

    Zhang Q 2008 Chin. Phys. B 17 125

    [6]

    Q Quan, J Kim 2006 International Journal of Computer Science and Network Security 6 157

    [7]

    Zurada J M 2000 Proc. of the 30th IEEE International Symposium on Multiple-Valued Logic, Portland, Oregon, May 23—25, 2000 p67

    [8]

    Zhang Z Y, Zhang Y 2008 Journal of Southeast University 38 18(in Chinese)[张志涌、张 昀 2008 东南大学学报 38 18]

    [9]

    Zhang Y, Zhang Z Y 2010 Proceedings of 2010 Sixth International Conference on Natural Computation Yantai, China, Aug.10—12, 2010, 1079

    [10]

    Liu Y, You Z 2008 Neurocomputing 71 3595

    [11]

    Zhou W, Zurada J M 2009 Neurocomputing 72 3782

    [12]

    Gupta M M, Liang Jin, Noriyasu Homma 2003 Static and Dynamic Neural Networks:From Fundamentals to Advanced Theory(New Jersey:IEEE Press)

  • [1]

    Gao H S, Zhang J 2008 Fourth International Conference on Natural Computation, Jinan, China, October 18—20,2008, 560

    [2]

    Cui B T, Chen J, Lou X Y 2008 Chin. Phys. B 17 1670

    [3]

    Qiu F, Cui B T, Ji Y 2009 Chin. Phys. B 18 5203

    [4]

    Xiong T, Zhang B L 2005 Acta Phys. Sin. 54 2435(in Chinese)[熊 涛、张便利 2005 物理学报 54 2435]

    [5]

    Zhang Q 2008 Chin. Phys. B 17 125

    [6]

    Q Quan, J Kim 2006 International Journal of Computer Science and Network Security 6 157

    [7]

    Zurada J M 2000 Proc. of the 30th IEEE International Symposium on Multiple-Valued Logic, Portland, Oregon, May 23—25, 2000 p67

    [8]

    Zhang Z Y, Zhang Y 2008 Journal of Southeast University 38 18(in Chinese)[张志涌、张 昀 2008 东南大学学报 38 18]

    [9]

    Zhang Y, Zhang Z Y 2010 Proceedings of 2010 Sixth International Conference on Natural Computation Yantai, China, Aug.10—12, 2010, 1079

    [10]

    Liu Y, You Z 2008 Neurocomputing 71 3595

    [11]

    Zhou W, Zurada J M 2009 Neurocomputing 72 3782

    [12]

    Gupta M M, Liang Jin, Noriyasu Homma 2003 Static and Dynamic Neural Networks:From Fundamentals to Advanced Theory(New Jersey:IEEE Press)

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  • Received Date:  07 November 2010
  • Accepted Date:  16 March 2011
  • Published Online:  15 September 2011

Blind detection algorithm of complex multi-valued discrete Hopfield network

  • 1. College of Automation, Nanjing University of Posts & Telecommunications, Nanjing 210003, China

Abstract: A novel complex multi-valued discrete Hopfield neural network (CMDHNN) is proposed in this paper. A multi-valued discrete activation function and a new energy function for CMDHNN are constructed. The stabilities for multi-valued CMDHNN with asynchronous and synchronous operating modes are also analyzed seperately. The special energy functions own the ability to describe the dynamic characteristics of CMDHNN which the energy functions of existing references cannot explain. Meantime, these energy functions can make the true source signal vector correspond to the minimum point of the energy function of CMDHNN. Furthermore, to verify effectiveness of CMDHNN, the weighted matrix of CMDHNN is constructed by the specific cost function for the blind detection of signals. Simulation results show that the proposed CMDHNN can be used to blindly detect the dense MQAM constellation signals with shorter received signals and the global minimal value of the CMDHNN energy function is verified.

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