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.

Fault Diagnosis of Tolerance Analog Circuit Based on Wavelet Neural Network with PSO Algorithm

2013 ◽  
Vol 307 ◽  
pp. 312-315 ◽  
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Parameters Optimization ◽  
Bp Algorithm ◽  
Algorithm Performance ◽  
Fast Training ◽  
Diagnosis Method

For the Difficulties in fault diagnosis of tolerance analog circuit, a Wavelet Neural Network (WNN) diagnosis method based on Particle Swarm Optimization (PSO) algorithm is proposed. To overcome the deficiencies of the traditional BP algorithm using in WNN, PSO algorithm is introduced into the parameters optimization in WNN, and the velocity disturbance operator is embedded to ensure the particle out of the premature position for PSO algorithm performance. The simulation results show that the proposed method has the fast training rate, accurate diagnosis, without local convergence.

Fault Diagnosis Based on Neural Network Trained by PSO Algorithm with Information Sharing Strategy for Analog Circuit

2014 ◽  
Vol 687-691 ◽  
pp. 3320-3323
Author(s):  
Yan Ming Wei ◽  
Xu Sheng Gan ◽  
Xue Qin Tang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Sharing ◽  
Local Convergence ◽  
Analog Circuit ◽  
Pso Algorithm ◽  
The Neural Network ◽  
Sharing Strategy ◽  
Diagnosis Method ◽  
Improved Pso

To solve some problems in fault diagnosis for analog circuit, a neural network diagnosis method using improved PSO algorithm is proposed. For the appearance on local convergence and prematurity in the application of standard PSO algorithm, an information sharing strategy is introduced, and then improved PSO algorithm is used to train the neural network for overcoming the deficiency of BP algorithm. The simulation indicates that the proposed fault diagnosis method has the fast convergence and accuracy without local convergence and prematurity for analog circuit.

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.

Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

scholarly journals Diagnosis Kerosakan Litar Menggunakan Pspice dan Rangkaian Neural Tiruan

2012 ◽  
Author(s):  
Jasronita Jasni ◽  
Samsul Bahari Mohd Noor ◽  
Ribhan Zafira Abd Rahman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Power Supply ◽  
Pulse Width ◽  
Analog Circuit ◽  
Switch Mode Power Supply ◽  
Pulse Width Modulator ◽  
Switch Mode ◽  
The Individual

Kerosakan komponen di dalam satu litar sangat sukar dikesan dan mengambil masa yang lama untuk dikenalpasti. Kertas ini membentangkan kaedah mendiagnosis kerosakan litar menggunakan Rangkaian Neural Tiruan (RNT). Litar Pengayun dan Pulse Width Modulator yang merupakan sebahagian dari Switch Mode Power Supply telah digunakan sebagai litar kajian. Litar ini disimulasi menggunakan Pspice dan data voltan pada nod direkodkan dan digunakan dalam sistem rangkaian neural tiruan. Setelah dilatih, sistem ini berupaya mengenalpasti komponen yang rosak dengan mudah. Kata kunci: Diagnosis kerosakan, litar analog, rangkaian neural Faulty components in a circuit is difficult and time consuming to be identified. This paper presents a method of circiut fault diagnosis using artificial neural networks (ANN). Oscillator circuit with Pulse Width Modulator which is part of a Switch Mode Power Supply is used in this study. The system is trained and able to identify the individual faulty components with ease. Key words: Fault diagnosis, analog circuit, neural network

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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