Analog Circuit Fault Diagnosis Based on RBF Neural Network Optimized by PSO Algorithm

2010 ◽  
Author(s):  
He Wuming ◽  
Wang Peiliang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Circuit Fault Diagnosis

Information-Applied Technology in Fault Diagnosis Model of RBF Neural Network Based on PSO Algorithm for Analog Circuit

2014 ◽  
Vol 540 ◽  
pp. 452-455
Author(s):  
Xiao Hua Zhang ◽  
Hua Ping Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Connection Weight ◽  
Diagnosis Method ◽  
The Individual ◽  
Network Diagnosis

To improve the ability of fault diagnosis for analog circuit, a RBF neural network diagnosis method trained by an improved Particle Swarm Optimization (PSO) algorithm is proposed. In order to overcome the shortcoming of the traditional BP algorithm of RBF neural network, PSO algorithm is introduced to optimize the center, width and connection weight of RBF neural network. And the mutation operator is inserted to ensure the individual in swarm out of the local optimum. The simulation shows that the proposed modeling algorithm has the better convergence and diagnosis characteristics.

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 1048-1051
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

Application of Fuzzy Neural Network to Analog Circuit Fault Diagnosis

2012 ◽  
Vol 182-183 ◽  
pp. 1179-1183 ◽  
Author(s):  
Shi Guan Zhou ◽  
Zai Fei Luo
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuzzy Neural Network ◽  
Analog Circuit ◽  
Training Methods ◽  
Fuzzy Neural ◽  
Circuit Fault Diagnosis ◽  
Specific Implementation ◽  
The Mathematical Model ◽  
And Training

Considering the discreteness and non-linearity of the component parameter and the advancement and limitations of neural network in the analogous circuit fault diagnosis and as the combination of the fuzzy logic and neural network, the fuzzy neural network’s having the merits of both, involving learning, association, recognition, adaptation and fuzzy information processing, a method with fuzzy neural network for the analogous circuit fault diagnosis is proposed. In this paper, the structure and training methods of the fuzzy neural network are presented and the specific implementation of the diagnosis system is illustrated with examples. Simulation results show that the mathematical model has a better diagnostic effect. Compared with other methods, this diagnostic method, with the broad application prospect of its structure and method, is scientific, simple, and practical and so on.

Information-Applied Technology in the Model of RBF Neural Network Based on Adaptive Genetic Algorithm for Analog Circuit Fault Diagnosis

2014 ◽  
Vol 540 ◽  
pp. 456-459
Author(s):  
Hu Cheng Zhao ◽  
Hao Lin Cui ◽  
Zhi Bin Chen
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Rbf Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Connection Weight ◽  
Proposed Model ◽  
Applied Technology ◽  
Circuit Fault Diagnosis ◽  
Crossover And Mutation

To obtain the improvement of analog circuit fault diagnosis, a RBF diagnosis model based on an Adaptive Genetic Algorithm (AGA) is proposed. First an adaptive mechanism about crossover and mutation probability is introduced into the traditional genetic algorithm, and then AGA algorithm is used to optimize the network parameters such as center, width and connection weight. The experiment simulation indicates that the proposed model has exact diagnosis characteristic.

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