Research of Construction Subcontracting Enterprise Competence Based on GA-BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 2664-2667
Author(s):  
Xi Kang Yan ◽  
Jing Yu Wang
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Matlab Simulation ◽  
Five Dimensions ◽  
Enterprise Competence ◽  
Bp Neural Network Model

A new evaluation index system, which includes five dimensions is put forward to evaluate the competitiveness of construction subcontracting enterprise properly. Based on GA optimized BP neural network model,construction subcontracting enterprises’ competitiveness can be quantitative analysis systematically. Use of Matlab simulation analysis,research has shown that this system can well solve the problem of construction subcontracting enterprise competitiveness evaluation.

A Model on Supplier Selection in Green Supply Chain Based on BP Neural Network

2011 ◽  
Vol 143-144 ◽  
pp. 312-316 ◽  
Author(s):  
Dao Guo Li ◽  
Zhong Yuan Zhou ◽  
Chen Yang
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Bp Neural Network ◽  
Supplier Selection ◽  
Simulation Experiment ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Green Supply Chain ◽  
Evaluation Indexes ◽  
Bp Neural Network Model

Based on the complexity of the evaluation index for supplier selection in green supply chain, this paper puts forward the evaluation index system in green supply chain and presents BP neural network model to select supplier with evaluation indexes as BP neural network's input and the outcome of DEA/AHP model as BP neural network's expected output. It can be proved that the model can effectively select suppliers in green supply chain by simulation experiment with LIINGO 9.0 and MATLAB7.0.

Forecasting Utilities Management Performance Based on Discriminant Technology and BP Neural Network

2020 ◽  
pp. 1-8
Author(s):  
Xiaoman Liu
Keyword(s):  
Neural Network ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Public Utility ◽  
Evaluation Index System ◽  
Public Utilities ◽  
Evaluation Index ◽  
Management Performance ◽  
Performance Evaluation Index ◽  
Bp Neural Network Model

According to the internal laws and development trends of the management and operation of local public utilities in China, the performance level of local public utility management in our country is predicted, and the corresponding performance evaluation index system is established. It is for the reform of the local public utilities management system in China and related policies. The adjustment has certain positive significance. This paper uses pattern discriminant analysis technology and BP neural network model to build a local public utility management performance prediction model and uses 11 regions in the east, middle, and west of the country as samples to predict the local public utility management performance.

The Model of Evaluating Teaching Quality Based on BP Neural Network Algorithm

2015 ◽  
Vol 719-720 ◽  
pp. 1297-1301
Author(s):  
Lei Bai ◽  
Xiao Xin Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Hot Spot ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Research Field ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Neural Network Algorithm

Teaching quality evaluation plays a key role for universities to improve its teaching quality and becomes a hot spot research field for related researchers. In this paper, we established the evaluation model of teaching quality based on BP neural network. Firstly an evaluation index system of teaching quality is designed. Then, according to the system we design the structure of BP neural network, determine the parameters and give the algorithm description. Finally, we program and verify the validity of the model in MATLAB environment. The experimental results show that the model can evaluate teaching quality practically by the evaluation index.

Study on the Evaluation of Scientific Research Achievements Based on the BP Neural Network

2012 ◽  
Vol 170-173 ◽  
pp. 3436-3439
Author(s):  
Xiao Hui Hou ◽  
Lei Huang ◽  
Xue Fei Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
Evaluation Method ◽  
Scientific Research ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Evaluation Result ◽  
Set Up ◽  
Solution Efficiency

The scientific research achievements are evaluated based on the BP neural network method which is developed in this paper. According to the analysis and consult with the well-known experts, set up the evaluation index system of scientific research achievements, and based on it, the BP neural network model which is used to evaluate the scientific research achievements is established. Through an actual example, in order to improve the solution efficiency, use the Matlab software to solve the model and get the evaluation result of the scientific research achievements in the example. The evaluation result has high accuracy and could meet the basic actual needs. The evaluation method which is set up in this paper will benefit to our country's evaluation index system of the scientific research achievements and will promote the development of evaluation methods of the scientific research achievements.

Based on the BP Neural Network of Logistics Equipment Support Capability Evaluation

2012 ◽  
Vol 546-547 ◽  
pp. 1090-1094
Author(s):  
Jian Sheng Hao ◽  
Qi Zhi Huang ◽  
Shu Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Assessment Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Engineering Theory ◽  
Simulation Results ◽  
Capability Evaluation ◽  
Theory Research

In this paper, the system engineering theory research logistical equipment safeguard ability assessment method, and established the equipment support of the evaluation index system, using BP neural network can to approximate any nonlinear system advantage, based on the BP neural network of logistics equipment support capability evaluation model for logistics equipment safeguard the ability to provide a new method. The simulation results show that this method can ensure objectivity.

Demand Prediction of the Rarely Used Spare Parts Based on the BP Neural Network

2014 ◽  
Vol 519-520 ◽  
pp. 1513-1519 ◽  
Author(s):  
Hong Long Mao ◽  
Jun Wei Gao ◽  
Xi Juan Chen ◽  
Jin Dong Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Spare Parts ◽  
Back Propagation Algorithm ◽  
Demand Prediction ◽  
Consumption Data ◽  
Bp Neural Network Model

For the rarely used spare parts, as the traditional predicting methods can't keep the high accurateness, the BP neural network is used to predict the rarely used spare parts demand. Firstly, the rarely used spare parts definition and its characteristics are given in this paper. Then the three layer BP neural network model is established, the back propagation algorithm is used as the learning algorithm. Finally, the rarely used spare parts-bus coupler consumption data is used for simulation analysis based on Guangzhou Subway line 3. The results show that the prediction is good.

Engineering Project Risk Evaluation System Construction and Research Based on BP Model

2013 ◽  
Vol 368-370 ◽  
pp. 2050-2053
Author(s):  
Jian Wei Zhang ◽  
Dong Lu Ye ◽  
Guan Chan Ye ◽  
Jing Zhi Zhou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
Risk Evaluation ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Engineering Project ◽  
Project Risk ◽  
Engineering Construction ◽  
Advantages And Disadvantages

According to the status of the current engineering construction field in our country, in order to adapt to the requirements of engineering construction project risk evaluation, this paper discuss establishing a reasonable risk evalluation index system and a model of effective risk evaluation. By analyzing the advantages and disadvantages of the general model of risk evaluation, determine the risk evaluation model combined with BP neural network with AHP; secondly,establish a risk evaluation index system; once again,illustrate the method which represents the correlation between evaluation index system and the degree of risk; finally, establish a reasonable BP neural network model.Key Word:Evaluation index; Risk Evaluation ;AHP;BP Neural Network; Model Construction

BP Neural Network Model Selection of Software Reliability Based on MATLAB

2015 ◽  
Vol 727-728 ◽  
pp. 991-995
Author(s):  
Shao Yun Song ◽  
Mao Luo ◽  
Hong Ming Zhou
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Bp Neural Network ◽  
Matlab Simulation ◽  
Improved Method ◽  
Simulation Tools ◽  
The Neural Network ◽  
Bp Neural Network Model ◽  
Model Year ◽  
Selection Of

Research and analysis of the BP neural networkstructure and features. Find its shortcomingsand propose an improved method for the deficiencies, and establish the neural network softwarereliability of the new model.Through MATLAB simulation tools forexamples of simulation, confirmed the new model year with the traditional modelof high-precision, the characteristics of generalization stronger.

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