Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree
Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the origin...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/6565737 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547463246905344 |
|---|---|
| author | Siyu Ji Chenglin Wen |
| author_facet | Siyu Ji Chenglin Wen |
| author_sort | Siyu Ji |
| collection | DOAJ |
| description | Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the original model parameters will cause the output of the model to deviate greatly from the true value. Traditional methods such as gradient descent and least squares methods are all centralized, making it difficult to adaptively update model parameters according to system changes. Firstly, in order to adaptively update the network parameters, this paper introduces the evaluation function and gives a new method to evaluate the parameters of the function. The new method without changing other parameters of the model updates some parameters in the model in real time to ensure the accuracy of the model. Then, based on the evaluation function, the Mean Impact Value (MIV) algorithm is used to calculate the weight of the feature, and the weighted data is brought into the established fault diagnosis model for fault diagnosis. Finally, the validity of this algorithm is verified by the example of UCI-Combined Cycle Power Plant (UCI-ccpp) simulation of standard data set. |
| format | Article |
| id | doaj-art-c227291bd5e040548049308e586e8948 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-c227291bd5e040548049308e586e89482025-02-03T06:44:38ZengWileyJournal of Control Science and Engineering1687-52491687-52572018-01-01201810.1155/2018/65657376565737Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution DegreeSiyu Ji0Chenglin Wen1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaNeural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the original model parameters will cause the output of the model to deviate greatly from the true value. Traditional methods such as gradient descent and least squares methods are all centralized, making it difficult to adaptively update model parameters according to system changes. Firstly, in order to adaptively update the network parameters, this paper introduces the evaluation function and gives a new method to evaluate the parameters of the function. The new method without changing other parameters of the model updates some parameters in the model in real time to ensure the accuracy of the model. Then, based on the evaluation function, the Mean Impact Value (MIV) algorithm is used to calculate the weight of the feature, and the weighted data is brought into the established fault diagnosis model for fault diagnosis. Finally, the validity of this algorithm is verified by the example of UCI-Combined Cycle Power Plant (UCI-ccpp) simulation of standard data set.http://dx.doi.org/10.1155/2018/6565737 |
| spellingShingle | Siyu Ji Chenglin Wen Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree Journal of Control Science and Engineering |
| title | Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree |
| title_full | Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree |
| title_fullStr | Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree |
| title_full_unstemmed | Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree |
| title_short | Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree |
| title_sort | data preprocessing method and fault diagnosis based on evaluation function of information contribution degree |
| url | http://dx.doi.org/10.1155/2018/6565737 |
| work_keys_str_mv | AT siyuji datapreprocessingmethodandfaultdiagnosisbasedonevaluationfunctionofinformationcontributiondegree AT chenglinwen datapreprocessingmethodandfaultdiagnosisbasedonevaluationfunctionofinformationcontributiondegree |