一种具有正弦基函数权值的反馈型神经网络模型

提出了一种新的两层反馈型神经网络模型. 该网络采用正弦基函数作为权值, 神经元激活函数为线性函数, 连接形式为两层反馈型结构. 研究并定义了该反馈型神经网络的能量函数, 分析了网络运行的稳定性问题, 并证明了在Liapunov意义下网络运行的稳定性. 网络运行过程中, 其权值不做调整(但随时间按正弦规律变化), 网络状态不断地转换. 随着网络状态变化其能量不断减小, 最终在达到稳定时能量到达极小点. 由于该反馈型神经网络权值为正弦函数, 特别适合于周期信号的自适应逼近和检测, 为实际中周期性信号检测与处理提供了一种新的、有效的网络模型和方法. 作为应用实例把该网络应用于电力系统中电压凹陷特征量实时检测, 仿真结果表明, 网络用于信号检测不仅有很高的静态精度, 而且有非常好的动态响应特性.

A feedback neural network with weights of sinusoidal functions

A new feedback neural network model is proposed. The network has the sinusoidal basis functions as its weights. Neuronal activation function is a linear function. The network connection form is feedback structure. An energy function is defined for the feedback neural network. And then, the network stability issue in operation is analyzed. In the Liapunov sense, the proposed feedback network stability is proved. During the operation of the network, the network states are changed ceaselessly but network weights vary according to time-dependent sinusoidal law. As the network state changes continuously, its energy will be reduced. Finally, when network comes to a stable state, its energy arrivs at a minimum value. The network is particularly suited for the adaptive approximation and the detection for periodic signals because of its sinusoidal basis function weights. It is, in practice, a new and effective way for periodic signal detection and processing. The very good detection results are obtained in the detection of power system voltage sag characteristics. Simulation examples show that the dynamic response speed of the network is very high.