The Research of Teaching Quality Evaluation Model Based on the Principal Component Analysis and Learning Vector Quantization

2020 ◽  
Author(s):  
Yang Ning ◽  
Kong Dehao
Keyword(s):  
Principal Component Analysis ◽  
Vector Quantization ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Principal Component ◽  
Component Analysis ◽  
Teaching Quality ◽  
Learning Vector Quantization ◽  
Model Based ◽  
Quality Evaluation Model

Intelligent online teaching system based on SVM algorithm and complex network

2020 ◽  
pp. 1-11
Author(s):  
Chuanxin Fang
Keyword(s):  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Research Work ◽  
Principal Component ◽  
Teaching Quality ◽  
Support Vector ◽  
School Teaching ◽  
The Impact ◽  
Quality Evaluation Model

English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.

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.

scholarly journals New model for flavour quality evaluation of soy sauce

2013 ◽  
Vol 31 (No. 3) ◽  
pp. 292-305 ◽  
Author(s):  
J. Feng ◽  
X.-B. Zhan ◽  
Z.-Y. Zheng ◽  
D. Wang ◽  
L.-M. Zhang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Soy Sauce ◽  
Complex Information

The soy sauce samples established a model for its flavour quality evaluation. Initially, 39 types of flavour compounds, organic acids and free amino acids in six different types of soy sauce were identified and determined by HS-SPME GC/MS and HPLC. The model was developed based on the principal component analysis method for assessing and ranking of flavour quality of soy sauce. Using the principal component analysis which simplifies complex information, our correlative evaluation model was established, tested by comparing the traditional sensory evaluation method, providing a new methodology for objective evaluation of the flavour quality of soy sauce.  

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