Predicting Student’s Performance Using Linear Kernel Principal Component Analysis and Recurrent Neural Network (LKPCA-RNN) Model

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.

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A novel radial basis function neural network for rainfall forecasting based on Kernel Principal Component Analysis

2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci.2012.6463271 ◽

2012 ◽

Author(s):

Jie Li ◽

Jiansheng Wu

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Radial Basis Function ◽

Basis Function ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Rainfall Forecasting ◽

Radial Basis

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

World Electric Vehicle Journal ◽

10.3390/wevj12040255 ◽

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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