A novel hybrid intelligent fault diagnosis method based on improved RBF neural network

Liu, Yi

doi:10.2991/icemi-16.2016.49

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Parameter design only needs to consider the extension constant and the weight of the output nodes. The network topology is shown in Figure 1 [5]. where X is the input data vector and Cn is the center of the data sample.…”

Section: Principles Of Radial Basis Function Neural Networkmentioning

confidence: 99%

“…It has good generalization ability and fast learning convergence speed. It has been successfully applied in nonlinear function approximation, time series analysis, data classification, pattern recognition, information processing, image processing, system modeling, control, and fault diagnosis [3][4][5][6][7]. The prediction method based on radial basis function neural networks has been widely studied and applied in other fields [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Yu,

Zheng,

Zhao

2023

Electronics

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A radial basis function (RBF) neural network-based calibration data prediction model for clock testers is proposed to address the issues of fixed calibration cycles, low efficiency, and waste of electrical energy. This provides a new method for clock tester traceability calibration. First, analyze the mechanism of clock tester calibration parameters and the influencing factors of prediction targets. Based on the learning rules of an RBF neural network, determine the data types of training and testing sets. Second, normalize the training and testing data to avoid the adverse effects of data characteristics and distribution differences on the prediction model. Finally, based on different prediction objectives, time-driven and data-driven calibration data prediction models are constructed using RBF neural networks. Through simulation analysis, it is shown that an RBF neural network is superior to a BP neural network in predicting clock tester calibration data, and time-driven prediction accuracy is superior to data-driven prediction accuracy. Moreover, the prediction error and mean square error of both prediction models are on the order of 10−9, meeting the prediction accuracy requirements.

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Section: Principles Of Radial Basis Function Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Yu,

Zheng,

Zhao

2023

Electronics

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“…The conventional RBF neural network has a certain number of hidden layer nodes and fixed network structure, the model uncertainties and time-varying parameters, it is difficult to achieve the identification of the global sense because of the uncertainties and time-varying parameters [13] . This paper uses an improved RBF neural network to realize adaptive adjustment of the number of hidden layer nodes, to make sure that RBF neural network is optimal [14][15][16] . The algorithm flowchart is shown in figure 4.…”

Section: Improvement Of Rbf Neural Networkmentioning

confidence: 99%

Cutting Constant Power Speed-adjusting Control System of Electrical Haulage Shearer Based on Rbf Neural Network Pid Control

Ding¹,

Li²,

Zhang³

2018

dtcse

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Abstract. According to the characteristics of electrical haulage shearer in the process of coal mining and the basic principle of cutting constant power speed-adjusting of electrical haulage shearer, taking the traction motor as the controlled object, and cutting constant power speed-adjusting control method of electrical haulage shearer based on RBF neural network PID control is proposed. Adaptive control method to achieve the electrical haulage shearer constant power speed-adjusting and high precision control. At the same time, improve the efficiency of coal mining, to ensure the safety of the shearer. The modeling and simulation analysis of the cutting constant power speed-adjusting control system of electrical haulage Shearer are carried out by Matlab/Simulink, and the system through the field experiment and the comparative analysis with traditional fuzzy PID control. The results show that the system based on RBF neural network PID control has stronger anti-interference ability, higher control precision, faster response time, stronger practicability and stability.

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A novel hybrid intelligent fault diagnosis method based on improved RBF neural network

Cited by 2 publications

References 13 publications

Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Cutting Constant Power Speed-adjusting Control System of Electrical Haulage Shearer Based on Rbf Neural Network Pid Control

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