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本文提出了一个新的复数多电平离散Hopfield神经网络,构造了新的适用于复数多电平离散神经网的激活函数和能量函数,分别讨论了异步与同步更新模式下神经网的稳定性.该能量函数不仅能描述文献能量函数不适用的复数多值Hopfield神经网的动力学特性,而且能保证待盲检测信号位于能量函数的最小值点.为验证CMDHNN的有效性,利用本文特有的性能函数下所构造的联结权阵盲检测MQAM信号.仿真试验表明:本算法仅需较短接收数据就可有效盲检测MQAM星座信号,仿真也证明了CMDHNN能量函数全局最小值的稳定性推论.
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关键词:
- 复数多电平离散Hopfield神经网络 /
- 盲检测 /
- MQAM信号
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.[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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[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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