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.

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.

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 An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

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.

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.

Comprehensive Evaluation of Implementation Effect on Later-Period Supportive Policy of Reservoir Resettlement Based on ANFIS

2014 ◽  
Vol 912-914 ◽  
pp. 1874-1878 ◽  
Author(s):  
Xin Xu ◽  
Xiao Yi Wang ◽  
Zhao Yang Wang ◽  
Yu Hang Long ◽  
Suo Jun Xu
Keyword(s):  
Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Nonlinear Characteristics ◽  
Comprehensive Evaluation Model ◽  
Implementation Effect ◽  
Reservoir Resettlement

According to engineering features of later-period supportive policy on reservoir resettlement, Economic evaluation index system of implementation effect on later-period supportive policy of reservoir resettlement is built to assess the implementation result of reservoir resettlement policy effectively in recent years. Considering the complexity of the evaluation index and the nonlinear characteristics of evaluation process, it is built that the comprehensive evaluation model of implementation effect on later-period supportive policy of reservoir resettlement based on ANFIS(Fuzzy artificial neural network) to provide the decision-making reference for implementation and improvement of later-period supportive policy of reservoir resettlement.

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