Performance Assessment on Two Kinds of GA-Based Neural Networks in Fault Diagnosis
| Periodical | Advanced Materials Research (Volumes 403 - 408) |
|---|---|
| Main Theme | MEMS, NANO and Smart Systems |
| Edited by | Li Yuan |
| Pages | 3090-3094 |
| DOI | 10.4028/www.scientific.net/AMR.403-408.3090 |
| Citation | Xiao Gang Jian et al., 2011, Advanced Materials Research, 403-408, 3090 |
| Online since | November, 2011 |
| Authors | Xiao Gang Jian, Jian Gxin Huang |
| Keywords | BPNN, Fault Diagnosis, GA, PNN, Transformer |
| Price | US$ 28,- |
In this paper, we analyze characteristics of two kinds of GA-Based neural networks. For large scale neural networks, it is necessary to optimize the initial network parameters. Using the global optimum ability of GA(Genetic Algorithm), we optimize the initial weights and biases of BPNN (Back-Propagation Neural Networks), which can avoid the local minimum. And we also optimize the spread coefficient of Gaussian Radial Basis Function of PNN (Probabilistic Neural Networks). Then the results in transformer fault diagnosis are compared. Experimental results based on Matlab show that the method of GA-Based greatly increases the reliability of diagnosis.