Singular Value Detection of Genetic Algorithm Optimizing RBF Neural Network

摘要

Due to the improper choosing of network weight, the center vector and the initial value of sound stage width vector of Gaussian function, when using RBF neural network to detect the singular value of the grid signal, it would lead to the decline of detection accuracy even to the RBF network divergent. Basing on the genetic algorithm, this study proposes a grid signal singular value detecting algorithm which is a genetic algorithm that can optimize RBF neural network and provides the mathematical model as well as detecting and analyzing the singular values of these conditions such as depression, heave, interruption and high frequency transient vibration in grid signals. The simulation results show that the proposed algorithm can detect the start and end time of various mutation singular values and it has certain application value in the power quality analysis of distributed generation synchronizing.