Design of Neural Network Output Layer in Fault Diagnosis of Analog Circuit

2007 ◽  
Author(s):  
Qu Haini ◽  
Xu Weisheng ◽  
Yu Youling
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Output Layer ◽  
Network Output

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.

Adaptive BP Wavelet Neural Network Method for Soft Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 307 ◽  
pp. 327-330
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Wavelet Neural Network ◽  
Learning Ability ◽  
Time Frequency ◽  
Soft Fault ◽  
Time Frequency Localization ◽  
Soft Fault Diagnosis

Because of the diversity and complexity of soft fault in analog circuit, the rapid and accurate diagnosis is very difficult. For this, an adaptive BP wavelet neural network diagnosis method of soft fault is proposed. It combines the time-frequency localization characteristics of wavelet and the self-learning ability of neural network in soft fault diagnosis of analog circuit, and by introducing the adaptive learning rate the diagnosis ability of BP wavelet neural network model can effectively be improved. In addition, PSPICE software is used to obtain the simulation data of actual analog circuit for the experiment. The results also verify the validity of the proposed 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

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