In this paper, a gradient method with momentum for sigma-pi-sigma neural networks (SPSNN) is considered in order to accelerate the convergence of the learning procedure for the network weights. The momentum coefficien...In this paper, a gradient method with momentum for sigma-pi-sigma neural networks (SPSNN) is considered in order to accelerate the convergence of the learning procedure for the network weights. The momentum coefficient is chosen in an adaptive manner, and the corresponding weak convergence and strong convergence results are proved.展开更多
Using the Faddeev-Jackiw （FJ） quantization method, this paper treats the CP^1nonlinear sigma model with ChernSimons term. The generalized FJ brackets are obtained in the framework of this quantization method, which ...Using the Faddeev-Jackiw （FJ） quantization method, this paper treats the CP^1nonlinear sigma model with ChernSimons term. The generalized FJ brackets are obtained in the framework of this quantization method, which agree with the results obtained by using the Dirac＇s method.展开更多
无线电频率占用度是表征无线电频谱真实占用状况的主要指标。针对目前国家规范测量无线电频率占用度以及频率占用度与无线信道状态关联研究存在的问题,建立了无线电信号发射与频谱接收系统,研究了无线电频率占用度测量方法与信道噪声模...无线电频率占用度是表征无线电频谱真实占用状况的主要指标。针对目前国家规范测量无线电频率占用度以及频率占用度与无线信道状态关联研究存在的问题,建立了无线电信号发射与频谱接收系统,研究了无线电频率占用度测量方法与信道噪声模型的关系。实验结果表明,如果信道是高斯噪声信道,可采用国标法进行频率占用度测量;如果信道是非高斯噪声信道,采用5西格玛原则统计频率占用度更好。采用机器学习方法研究了无线信道占用状况,结果表明,信噪比(Signal to Noise Ratio,SNR)大于15 dB时,基于信号频谱分类模型的5类无线电信号的分类准确率可达到99%以上;在低SNR和非高斯噪声信道下如何进一步提高无线电信号的分类准确率是一个挑战。展开更多
文摘In this paper, a gradient method with momentum for sigma-pi-sigma neural networks (SPSNN) is considered in order to accelerate the convergence of the learning procedure for the network weights. The momentum coefficient is chosen in an adaptive manner, and the corresponding weak convergence and strong convergence results are proved.
文摘Using the Faddeev-Jackiw （FJ） quantization method, this paper treats the CP^1nonlinear sigma model with ChernSimons term. The generalized FJ brackets are obtained in the framework of this quantization method, which agree with the results obtained by using the Dirac＇s method.
文摘无线电频率占用度是表征无线电频谱真实占用状况的主要指标。针对目前国家规范测量无线电频率占用度以及频率占用度与无线信道状态关联研究存在的问题,建立了无线电信号发射与频谱接收系统,研究了无线电频率占用度测量方法与信道噪声模型的关系。实验结果表明,如果信道是高斯噪声信道,可采用国标法进行频率占用度测量;如果信道是非高斯噪声信道,采用5西格玛原则统计频率占用度更好。采用机器学习方法研究了无线信道占用状况,结果表明,信噪比(Signal to Noise Ratio,SNR)大于15 dB时,基于信号频谱分类模型的5类无线电信号的分类准确率可达到99%以上;在低SNR和非高斯噪声信道下如何进一步提高无线电信号的分类准确率是一个挑战。
