The research based on BP neural network for the evaluation index system of science and technology projects funding

2011 ◽  
Author(s):  
Tianxiang Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
Science And Technology ◽  
Evaluation Index System ◽  
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.

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

scholarly journals Evaluation of Innovation Ability of Industrial Clusters Based on BP Neural Network

2021 ◽  
Vol 2074 (1) ◽  
pp. 012091
Author(s):  
Chunjie Fang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
International Competitiveness ◽  
Industrial Clusters ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Innovation Ability

Abstract In order to improve the innovation ability of enterprises and enhance international competitiveness, it is necessary to correctly analyze and evaluate the innovation ability of industrial clusters. Therefore, BP neural network is used to explain the innovation ability of industrial clusters, and the evaluation index system is established. By investigating industrial clusters and using it to provide references for the evaluation of industrial clusters’ innovation capabilities.

scholarly journals User Experience Evaluation of Cross-Channel Consumption: Based on Grounded Theory and Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hua Yang ◽  
Huiying Wei ◽  
Xiang He ◽  
Yue Yan ◽  
Xiaoju Liu
Keyword(s):  
Neural Network ◽  
Grounded Theory ◽  
User Experience ◽  
Index System ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Theoretical Research ◽  
Evaluation Index ◽  
Channel Switching ◽  
Depth Analysis

With the rapid development of e-commerce technology, cross-channel consumption has become the mainstream mode of contemporary consumers. However, there are several problems of cross-channel consumption such as inconsistency of online and offline channel information and service, disfluency of channel switching which have brought adverse effects on user experience. The question arises here as to what factors influence user experience and how to build a scientific and effective evaluation index system. Different from previous studies based on sellers, this paper used grounded theory to analyze and summarize the evaluation index system of user experience under cross-channel consumption from the perspective of consumers. We summarized and refined four first level indexes which are “online platform attribute, offline entity attribute, channel switching attribute, and individual demand” and 13 second level indexes which are “platform operation, platform information, platform service, platform promotion, product quality, service quality, environment quality, channel consistency, channel switching cost, channel switching fluency, psychological expectation, personal interests and individual needs.” Then, we used BP neural network to build the evaluation model and trained and simulated the performance of the sample. The results show that the evaluation model has a good generalization ability and can effectively evaluate user experience under cross-channel consumption. Finally, implications and limitations are also discussed. This study helps to enrich the theoretical research on user experience and consumer behavior. It also provides targeted basis for in-depth analysis of cross-channel consumption behavior, establishment of user experience evaluation index system, and improving user experience and multichannel management of physical stores.

scholarly journals Evaluating the Investment Climate for China’s Cross-Border E-Commerce: The Application of Back Propagation Neural Network

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 526
Author(s):  
Yi Lei ◽  
Xiaodong Qiu
Keyword(s):  
Neural Network ◽  
Index System ◽  
Evaluation System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Entropy Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Investment Climate ◽  
Cross Border

China’s cross-border e-commerce will usher in a new golden age of development. Based on seven countries which include the Russian Federation, Mongolia, Ukraine, Kazakhstan, Tajikistan, Kyrgyzstan and Belarus along the “Belt and Road”, an evaluation system for cross-border e-commerce investment climate indicators is established in this study. This research applied the entropy method twice to evaluate the investment climate of seven countries based on 5 years panel data comprehensively and these countries are then classified into politics-oriented and industry-oriented countries, and then the weight of indicators for each category is analyzed. In addition, cross-border e-commerce investors are proposed to prioritize industry-oriented countries. Back propagation neural network algorithm is used to map the existing data and optimize the evaluation index system in combination with the genetic algorithm. This research denotes the effort to find out the index evaluation combination corresponding to the best overall score, make the established evaluation index system applicable to other countries, and provide reference for cross-border e-commerce investors when evaluating the investment climate in each country. This study provides the important practical implications in the sustainable development of China’s cross-border e-commerce environment.

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.

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.

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