Subscribe Now Subscribe Today
Science Alert
Curve Top
Information Technology Journal
  Year: 2013 | Volume: 12 | Issue: 11 | Page No.: 2201-2206
DOI: 10.3923/itj.2013.2201.2206
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Singular Value Detection of Genetic Algorithm Optimizing RBF Neural Network

Li Jia-Sheng, Wang Ying-De and Tian Wang-Lan

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.
PDF Fulltext XML References Citation Report Citation
  •    Assessment of Gastric Cancer Survival: Using an Artificial Hierarchical Neural Network
  •    Optimization of Culture Medium for the Production of Poly-γ-glutamic Acid Using Artificial Neural Networks and Genetic Algorithms
  •    The Application Study of S-Transform Modulus Time-frequency Matrix in Detecting Power Quality Transient Disturbance
  •    Denoising of Power Quality Disturbance Based on Self-adapting Neural Fuzzy Control
How to cite this article:

Li Jia-Sheng, Wang Ying-De and Tian Wang-Lan, 2013. Singular Value Detection of Genetic Algorithm Optimizing RBF Neural Network. Information Technology Journal, 12: 2201-2206.

DOI: 10.3923/itj.2013.2201.2206






Curve Bottom