针对时差定位法受不同模式波速度差异及波形传播畸变等因素影响的问题,将神经网络技术应用到声发射源定位中。为了克服传统BP算法训练时间长和精度不够高的缺点,提出代数神经网络概念,在网络训练阶段引入代数算法,将复杂的非线性优化问...针对时差定位法受不同模式波速度差异及波形传播畸变等因素影响的问题,将神经网络技术应用到声发射源定位中。为了克服传统BP算法训练时间长和精度不够高的缺点,提出代数神经网络概念,在网络训练阶段引入代数算法,将复杂的非线性优化问题转化为简单的代数方程组求解问题,直接获得最优点,大大缩短了网络的学习时间。同时作为定位特征研究分析了转子碰摩声发射信号的非线性动力学特性,提出了关联维数、最大Lyapunov指数和K o lm ogorov熵等声发射源的非线性动力学新特征,并将其作为神经网络的输入定位特征。实验结果表明,利用这些声发射源的非线性动力学特征和神经网络结合能较好地解决了碰摩声发射源定位问题,为转子碰摩故障诊断提供依据,具有良好的应用前景和进一步研究的价值。展开更多
A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-base...A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.展开更多
文摘针对时差定位法受不同模式波速度差异及波形传播畸变等因素影响的问题,将神经网络技术应用到声发射源定位中。为了克服传统BP算法训练时间长和精度不够高的缺点,提出代数神经网络概念,在网络训练阶段引入代数算法,将复杂的非线性优化问题转化为简单的代数方程组求解问题,直接获得最优点,大大缩短了网络的学习时间。同时作为定位特征研究分析了转子碰摩声发射信号的非线性动力学特性,提出了关联维数、最大Lyapunov指数和K o lm ogorov熵等声发射源的非线性动力学新特征,并将其作为神经网络的输入定位特征。实验结果表明,利用这些声发射源的非线性动力学特征和神经网络结合能较好地解决了碰摩声发射源定位问题,为转子碰摩故障诊断提供依据,具有良好的应用前景和进一步研究的价值。
基金The National Natural Science Foundation of China(No.61771126,61372104)the Science and Technology Project of State Grid Corporation of China(o.SGRIXTKJ[2015] 349)
文摘A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.
