scholarly journals Analysis of College Student Registration Management and Change Prediction Based on Mutated Fuzzy Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Wang ◽  
Lie Jiao ◽  
Chunzhi Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Correct Prediction ◽  
Proposed Model ◽  
School Teaching ◽  
Fuzzy Neural ◽  
Optimization Function ◽  
Model Training

Nowadays, a large number of students' academic registrations change every year in universities, but most of these cases are recorded and mathematically and statistically analysed through forms or systems, which are cumbersome and difficult to find some potential information in them. Therefore, timely and effective prediction of student registration changes and early warning of student registration changes by technical means is an important part of university registration management. At present, relevant research is mostly based on mathematical statistical analysis methods such as students' current credit evaluation or course score averages and seldom uses data mining and other technical methods for in-depth research. In this paper, we propose a mutated fuzzy neural network (MFNN) based prediction model for student registration changes in colleges and universities, which can provide supplementary reference decisions for school registration management for school teaching managers. In this paper, we first construct the corresponding prediction model of academic registration variation, define the relevant parameters, and model the optimization problem and propose the objective optimization function. Second, the proposed model is optimized by adding principal component analysis (PCA) to the original model to improve the efficiency of model training and the correct prediction rate. It is verified that the proposed model can effectively predict individual students' academic registration changes with a prediction accuracy of nearly 92.91%.

Surface Roughness Prediction Model of Cylinder Grinding

2012 ◽  
Vol 157-158 ◽  
pp. 123-126 ◽  
Author(s):  
Ning Ding ◽  
Yi Chen Wang ◽  
Ding Tong Zhang ◽  
Yu Xiang Shi ◽  
Jian Shi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Vibration Signal ◽  
Feed Speed ◽  
Vibration Data ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction

Based on the theory of roughness during cylinder grinding and the theory of fuzzy-neural network, a surface roughness intelligent prediction model is developed in this paper. The feed, speed, and the vibration data are the inputs for the model. An accelerometer is used to gather the vibration signal in real time. The model is used in the grinding experiment, and verifies the feasibility of the proposed model.

Surface Roughness Prediction Model Based on AE in Grinding

2014 ◽  
Vol 701-702 ◽  
pp. 150-153
Author(s):  
Ning Ding ◽  
Wen Ze Yu
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Surface Roughness Prediction ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction ◽  
Ae Signals

Based on the theory of roughness during grinding and the theory of fuzzy-neural network, a new intelligent prediction model is developed in this paper. The inputs for the model are the grinding parameters and the AE signals. Beijing Shenghua SAEU2S system was used to collect and analyze the signals of acoustic emission. The experiment was conducted, and the results verify the feasibility of the proposed model.

scholarly journals Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoxu Chen ◽  
Linyuan Wang ◽  
Zhiyu Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Corrosion Rate ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Internal Corrosion ◽  
Fuzzy Neural

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

Fuzzy neural network based prediction model applied in primary component analysis

2017 ◽  
Vol 20 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Xiaohong Peng ◽  
Shiyi Xie ◽  
Yinghuai Yu ◽  
Zhenlu Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Component Analysis ◽  
Primary Component ◽  
Fuzzy Neural

Closed Loop Dynamic Fuzzy Neural Network for Real-Time Lifetime Forecasting

2013 ◽  
Vol 834-836 ◽  
pp. 1074-1080
Author(s):  
Wen Wang Li ◽  
Gao Feng Zheng ◽  
Jian Yi Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Real Time ◽  
Fuzzy Neural Network ◽  
Closed Loop ◽  
Failure Time ◽  
Membrane Function ◽  
The Real ◽  
Fuzzy Neural ◽  
Monitoring Information

Real-time lifetime forecasting has extensive application in the fields of machine system manufacturing and integration, which is a good way to promote the dependability and operation stability. In this paper, a closed loop adaptive forecasting model with feedback channel of state monitoring information is built up for the real-time lifetime forecasting. The difference of working state between prediction and monitoring information is used to evaluate the prediction performance. The dynamic fuzzy neural network introduced into the prediction model, in which the fuzzy rule, membrane function and structure parameters can be adjusted according to the evaluate results. A service lifetime testing experiment of gear case is utilized to validate the prediction model. The proposed model achieved reasonable precision with an error of less than 1 hour between the failure time of experimental results and the forecasting remaining lifetime. The adaptive prediction method can deal with the real-time lifetime forecasting for multiple factors and nonlinear system without specific parameters structure.

scholarly journals Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaochen Zhang ◽  
Hongli Gao ◽  
Haifeng Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Performance Degradation ◽  
Ball Screw ◽  
Degradation Model ◽  
Quantum Genetic Algorithm ◽  
Feature Vectors ◽  
Fuzzy Neural

To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA) and dynamic fuzzy neural network (DFNN) is studied. The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object. Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance. First, screw vibration signal features are extracted both in time domain and frequency domain. Then the feature vectors can be obtained by principal component analysis (PCA). Second, the initialization parameters of the DFNN are optimized by means of QGA. Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model. The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.

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