Neural Network Model and Design Patterns on Fuzzy Evaluation System of Special Electromechanical Equipment

2012 ◽  
Vol 510 ◽  
pp. 239-243
Author(s):  
Jian Ping Ye ◽  
Lin Xiang Shi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Design Patterns ◽  
Evaluation System ◽  
Behavior Pattern ◽  
Fuzzy Evaluation ◽  
Mvc Pattern ◽  
Main Class ◽  
Electromechanical Equipment

According to the physical characteristics and safety requirements, the evaluation levels of special electromechanical equipment were created. The five-layer neural network model was created according to the multi-layer neural network model. The first layer is input layer, the last layer is output layer, and the others are hidden layers. The software structure of evaluation system was designed, and the main class diagram was designed with UML. The relations among views, data model and dispatch controller were designed with MVC pattern. The factory method was used to instantiate view objects according to the object creation pattern. The ITERATOR pattern of structural pattern was used to find out view objects in the view object aggregation. The strategy pattern of behavior pattern was used to encapsulate different neural network algorithms.

Application of the Back Propagation Neural Network Model on the Supplier Evaluation in E-Manufacturing Environment

2009 ◽  
Vol 16-19 ◽  
pp. 174-177
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Ilias Oraifige
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Supplier Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Supplier Evaluation ◽  
Test Results ◽  
Model Training

The supplier evaluation is a key section of the intelligent internet supplier selection & evaluation system. The model used for supplier evaluation is Back Propagation Neural Network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It provides a outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, Training of the BP NN model and test results.

Evaluation System Based on Neural Network Model

2017 ◽  
Vol 4 (0) ◽  
pp. 1
Author(s):  
Xu Lin ◽  
Xie Chao-Fan ◽  
Xu Lu-Xiong
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

The Assemblability Evaluation of Automotive Glass Windshield Mold Based on Fuzzy Neural Network

2010 ◽  
Vol 139-141 ◽  
pp. 1753-1756
Author(s):  
Lai Teng ◽  
Li Zhong Wang ◽  
De Hong Yu ◽  
Shun Lai Zang ◽  
Yu Jiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Urgent Problem ◽  
Mapping Model ◽  
Fuzzy Neural

Nowadays the production of mold seriously restricts the manufacture of products as well as the development of new products, it has become an urgent problem to be solved. The paper mainly discussed the fuzzy neural network model and learning algorithm, and utilized expert evaluating system to acquire the training and test samples. Moreover, it established the related mapping model for fuzzy neural network to evaluate the assemblability of mold, so as to improve the productivity of mold. By adopting two different fuzzy neural networks to contrast and evaluate the assemblability evaluation system of the parts of windshield mold, it was concluded that the improved fuzzy neural network model had advantage over the conventional one. Finally, the satisfactory results of assemblability evaluation system of windshield mold had been achieved by coming with examples to carry out error analysis of the assemblability evaluation system.

A Method of Network Security Situation Prediction Based on Gray Neural Network Model

2011 ◽  
Vol 63-64 ◽  
pp. 936-939 ◽  
Author(s):  
Nian Liu ◽  
Geng Li ◽  
Yong Liu
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Prediction Method ◽  
Response Strategy ◽  
Network Information ◽  
Intelligent Analysis ◽  
Bp Neural Network Model

In this paper, a new network security situation intelligent analysis prediction method is proposed, which applies GM(1,1) model and BP neural network model in the analytic prediction field of network security situation information, and combination and optimization is performed to it to improve the accuracy of network security situation prediction. By analyzing and calculating the great amount of information acquired from network security situation evaluation system, it is able to make prediction on the current security situation of network system and the its future change trend, and make and implement relative response strategy according to prediction results, and reduce the harm from network attacks and improve the emergency response ability of network information system, so that we can make preparation before great damage occurs and reduce or avoid any possible attack to ensure the smooth running of system. The experiment results show that this method is a better solution for network security situation prediction.

scholarly journals An Adaptive BP Neural Network Model for Teaching Quality Evaluation in Colleges and Universities

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yong Jin ◽  
Yiwen Yang ◽  
Baican Yang ◽  
Yunfu Zhang
Keyword(s):  
Neural Network ◽  
Nonlinear Problem ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Teaching Quality ◽  
Teacher Assessment ◽  
University Teaching ◽  
Gradient Descent Method ◽  
Fuzzy Evaluation

There is currently no fair, rational, or scientific approach for evaluating college teachers’ teaching abilities. Mathematical methods are frequently used to measure the teaching capacity of college instructors in order to make it more scientific. Traditional statistical analysis evaluation models, fuzzy evaluation methods, grey decision methods, and the analytic hierarchy process (AHP) are only a few examples. Because teacher assessment is a nonlinear problem, even though the preceding methods have produced some positive results, they are vulnerable to some subjectivity. In this paper, the neural network model is incorporated into the adaptive vector and momentum of the modified BP neural network of a gradient descent method to boost the model’s convergence speed, and the model is thoroughly researched to evaluate university teaching quality, and the network structure is omitted to address the complex nonlinear problem of college and university teaching quality assessment. The model’s comprehensive evaluation of teaching activities is then bolstered by the addition of new evaluation indexes to the existing ones.

scholarly journals Intuitionistic fuzzy evaluation of artificial neural network model

2021 ◽  
Vol 27 (4) ◽  
pp. 71-77
Author(s):  
Todor Petkov ◽  
◽  
Veselina Bureva ◽  
Stanislav Popov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Medical Data ◽  
Fuzzy Evaluation ◽  
Intuitionistic Fuzzy ◽  
Artificial Neural

In this paper a method that evaluates a trained artificial neural network is presented. The learning type of an artificial neural network is supervised learning which requires labeled input training vectors. Labeled medical data is provided to train the network, where the labels can either be 1 if the person is alive, or 0 if the person has deceased. The data is divided into training and validation vectors. The validation input vectors are used to evaluate the model and the results are summarized by using intuitionistic fuzzy values.

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