摘要
面向中性点直接接地电力系统发生的铁磁谐振过电压所显现的混沌特性,在径向基函数神经网络的基础上,提出引进一种极大熵学习算法对该混沌系统进行控制.该方法通过最优化一个目标函数导出中心向量的学习规则,充分利用网络隐层的聚类功能,极大改善网络的回归和学习能力.对具体的铁磁谐振系统的数值实验证实了该方法在针对铁磁谐振过电压混沌控制中的有效性和可行性.
Facing to the ferroresonance over voltage of neutral grounded power system, an improved learning algorithm based on RBF neural networks is used to control the chaos system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers. It improves the regression and learning ability of neural networks. The academic derivation testifies the errors and precision could satisfy demand of chaos control. And simulation calculation also displayed that the rate of convergence of the improved RBF neural network is much quickly and approach ability is much stronger. The numerical experimentation of ferroresonance system testifies the reliability and stability of using the algorithm to control chaos in neutral grounded power system.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2006年第11期5714-5720,共7页
Acta Physica Sinica
关键词
中性点直接接地系统
混沌控制
径向基函数
极大熵原理
neutral grounded power system, chaos control, radial basis function, maximum-entropy principle
