Elman neural network based short-term photovoltaic power forecasting using association rules and kernel principal component analysis

2018 ◽  
Vol 10 (4) ◽  
pp. 043501 ◽  
Author(s):  
Chunxia Dou ◽  
Hang Qi ◽  
Wei Luo ◽  
Yamin Zhang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Association Rules ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Short Term ◽  
Elman Neural Network ◽  
Photovoltaic Power ◽  
Power Forecasting

Classification of Area Flowing Based on KRNN Method

2011 ◽  
Vol 255-260 ◽  
pp. 2855-2859
Author(s):  
Xiang Sun
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Kernel Principal Component Analysis ◽  
Analysis Method ◽  
Graduate Employment ◽  
Principal Component Analysis Method ◽  
The Neural Network

It is hard to search the influence variables and to classify the flowing areas of graduate employment due to the complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the classification result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the flowing areas of graduate employment is tried to be classified, and the complex factor problem has been well dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data, it is shown that the proposed methods can both achieve good classification performance comparing with NN method. And the Kernel Principal Component Analysis method performs better than the Principal Component Analysis method.

scholarly journals Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

2021 ◽  
Vol 12 (4) ◽  
pp. 255
Author(s):  
Shuna Jiang ◽  
Qi Li ◽  
Rui Gan ◽  
Weirong Chen
Keyword(s):  
Neural Network ◽  
Water Management ◽  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Vector Quantization ◽  
High Dimension ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Vector Quantization ◽  
Kernel Principal Component Analysis

To solve the problem of water management subsystem fault diagnosis in a proton exchange membrane fuel cell (PEMFC) system, a novel approach based on learning vector quantization neural network (LVQNN) and kernel principal component analysis (KPCA) is proposed. In the proposed approach, the KPCA method is used for processing strongly coupled fault data with a high dimension to reduce the data dimension and to extract new low-dimensional fault feature data. The LVQNN method is used to carry out fault recognition using the fault feature data. The effectiveness of the proposed fault detection method is validated using the experimental data of the PEMFC power system. Results show that the proposed method can quickly and accurately diagnose the three health states: normal state, water flooding failure and membrane dry failure, and the recognition accuracy can reach 96.93%. Therefore, the method proposed in this paper is suitable for processing the fault data with a high dimension and abundant quantities, and provides a reference for the application of water management subsystem fault diagnosis of PEMFC.

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