A novel Local BP Neural Network model and application in parameter identification of power system

The traditional mathematical modeling is nonrepresentational and it is hard for understanding. In oder to model the real system in a intuitive method, a novel Local BP Neural Network (LBPNN) model has been proposed to imitate arbitrary feed-forward topologies of networks and the weights' traini...

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Bibliographic Details
Published inProceedings of the 33rd Chinese Control Conference pp. 6775 - 6780
Main Authors Qian Kun, Wang Tian-zhen, Tang Tian-hao, Claramunt, Christophe
Format Conference Proceeding
LanguageEnglish
Published TCCT, CAA 01.07.2014
Subjects
constrained learning
Electronic mail
Local Neural Network
Mathematical model
Neural networks
Noise
Power systems
stochastic gradient
Stochastic processes
Training
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Summary:The traditional mathematical modeling is nonrepresentational and it is hard for understanding. In oder to model the real system in a intuitive method, a novel Local BP Neural Network (LBPNN) model has been proposed to imitate arbitrary feed-forward topologies of networks and the weights' training algorithm-constrained stochastic gradient descent (CSGD) is also introduced in this paper. The network model could be used to approach functions which could improve the training speed and reduce the training complexity. With the LBPNN model and the CSGD training algorithm, network's parameter of weight could be identified within constrains. The training algorithm's robustness and effectiveness are verified in the LBPNN. Finally, the LBPNN model is used to map the fuzzy petri net(FPN) of a power system and the parameters of weights in the FPN are identified by training the LBPNN model with the CSGD algorithm.
ISSN:2161-2927
DOI:10.1109/ChiCC.2014.6896115