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.

GA Optimized Binary Tree SVM for Analog Circuit Fault Diagnosis

2012 ◽  
Vol 235 ◽  
pp. 423-427 ◽  
Author(s):  
Bao Yu Dong ◽  
Guang Ren
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Binary Tree ◽  
Analog Circuit ◽  
Wavelet Packet ◽  
Principal Component ◽  
Monte Carlo Analysis ◽  
Support Vector ◽  
Roulette Wheel Selection ◽  
Circuit Fault Diagnosis

This paper presents a novel method of analog circuit fault diagnosis based on genetic algorithm (GA) optimized binary tree support vector machine (SVM). The real-valued coding genetic algorithm is used to optimize the binary tree structure. In optimization algorithm, we use roulette wheel selection operator, partially mapped crossover operator, inversion mutation operator. In simulation experiment, we use Monte-carlo analysis for 40kHz Sallen-Key bandpass filter and get transient response of ten faults. Then we extract feature vector by db3 wavelet packet transform and principal component analysis (PCA), and diagnose circuit faults by different SVM methods. Experiment results show the proposed method has the better classification accuracy than one-against-one (o-a-o), one-against-rest (o-a-r), Directed Acyclic Graph SVM (DAGSVM) and binary tree SVM (BT-SVM). It is suitable for practical use.

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.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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