Yang Jiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yang Jiang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chaochun Zhong
College of Quality & Safety Engineering, China Jiliang University, Hangzhou 310018, China
1 shared publication - Fan Zheng
Zhejiang Institute of Metrology, Key Laboratory of Energy and Environmental Protection Measurement of Zhejiang Province, Hangzhou 310007, China
1 shared publication - Jiayan Chen
College of Quality and Standardization, China Jiliang University, Hangzhou 310018, China
1 shared publication - Juan Zhou
College of Quality and Standardization, China Jiliang University, Hangzhou, China
1 shared publication - Limin Wang
Zhejiang Institute of Metrology, Key Laboratory of Energy and Environmental Protection Measurement of Zhejiang Province, Hangzhou 310007, China
1 shared publication - Tao Hong
College of Quality & Standardization, China Jiliang University, Hangzhou, 310018
1 shared publication
Latest Publications
- Open Access
- 13 February 2023
- Published by MDPI AG in Electronics
- Vol. 12 (4) , 932
Improved MLP Energy Meter Fault Diagnosis Method Based on DBN
In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we first use DBN to strengthen the feature extraction ability of the network and solve the problem of many kinds of feature data and high dimensionality of historical data. After that, the processed feature information is input into the MLP neural network, and the strong processing ability of MLP for nonlinear numbers is used to solve the problem of weak correlation among data in the historical data set and improve the accuracy rate of faults diagnosis. The final results show that the DBN-MLP method used in this paper can effectively reduce the number of training iterations to reduce the training time and improve the accuracy of diagnosis.
- 13 February 2023
- Published by MDPI AG in Electronics
- Vol. 12 (4) , 932
Open AccessImproved MLP Energy Meter Fault Diagnosis Method Based on DBN
In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we first use DBN to strengthen the feature extraction ability of the network and solve the problem of many kinds of feature data and high dimensionality of historical data. After that, the processed feature information is input into the MLP neural network, and the strong processing ability of MLP for nonlinear numbers is used to solve the problem of weak correlation among data in the historical data set and improve the accuracy rate of faults diagnosis. The final results show that the DBN-MLP method used in this paper can effectively reduce the number of training iterations to reduce the training time and improve the accuracy of diagnosis.