Analysis of enterprise site selection and R&D innovation policy based on BP neural network and GIS system

2020 ◽  
Vol 39 (4) ◽  
pp. 5609-5621
Author(s):  
Li Yonghui ◽  
Bai Lipeng ◽  
Cheng Bo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Spatial Data ◽  
Site Selection ◽  
Innovation Policy ◽  
Science And Technology ◽  
Spatial Optimization ◽  
Entropy Weight ◽  
The Government ◽  
Gis System

The traditional spatial optimization location solution is difficult to solve the space optimization location problem under the condition of large data volume. However, GIS has the advantage of analyzing and processing spatial data, which can effectively compensate for this defect. In this paper, we analyze the enterprise site selection and R&D innovation policy based on BP neural network and GIS system. As a tool for the government to guide, encourage, support and adjust innovation activities and application of achievements, science and technology policy can provide new support for the development of innovation by improving the industrial chain and innovating the industrial structure. Moreover, the quantitative analysis of the entropy weight method and the qualitative analysis of the AHP method are combined to analyze a number of influencing factors. Based on this, the overlay of various factors is further analyzed, and the maximum eigenvalues of the target layer and the criterion layer and the weights of each index are calculated using MATLAB tools. Therefore, according to the different characteristics of different periods and different fields, the government should formulate science and technology innovation policies to improve the specificity and applicability of the policies.

scholarly journals ANALYSIS OF THE LOCATION OF NANNING LARGE-SCALE MALL BASED ON BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 975-979
Author(s):  
H. Huang ◽  
L. L. Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Selection ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Location Analysis ◽  
Analysis Model ◽  
Reference Information ◽  
Neural Network Algorithm

Abstract. Site selection is a key first step in the operation of large-scale shopping malls, and most of the existing site selection methods lack practicality and efficiency. Therefore, it is necessary to carry out a scientific modeling of the site selection problem and provide effective reference information for site selection. With the development of machine learning algorithms, the modeling of such problems becomes more and more simple. In this paper, using matlab software as a tool, based on BP neural network algorithm, Nanning urban area is selected as the research object. After analyzing the influencing factors of location problem, the large-scale mall location analysis modeling is carried out. After repeated training and testing of the training data and the test data, the data for testing the usability is input into the model and applied for analysis. It turns out that the large-scale mall location analysis model is usable and can meet the site selection needs of the mall.

Cost Estimation of Post-Earthquake Rehabilitation of Industries Based on BP Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 1789-1792
Author(s):  
Yan Ru Wang ◽  
Mao Yu Zhang ◽  
Yu Xia
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Rapid Recovery ◽  
Service Projects ◽  
Industrial Projects ◽  
The Government

After the destructive earthquake, it is urgent that the damaged houses, infrastructure and industrial projects and other related equipment and facilities are to be rehabilitated and restored. How to carry out reconstruction investment plans of various sectors became a major concern to the government. To meet the needs of rapid recovery of production and life in affected areas, the method based on BP neural network is proposed to estimate the post-earthquake rehabilitation cost of industrial, agricultural and service projects. That method considers the macro-economic characteristics of every damaged area. It can provide some guidance for the government that need to make decisions of post-earthquake recovery and reconstruction plans.

The organization of masses’ spontaneous sports activities based on BP neural network model

2021 ◽  
pp. 1-9
Author(s):  
Xiangling Wang ◽  
Xiangying Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Chinese Government ◽  
Beijing City ◽  
The Government ◽  
The Masses ◽  
Bp Neural Network Model ◽  
Sports Activities

Chinese government has made huge efforts in improving people’s spiritual and cultural life, but effects are far from satisfactory. The organization of masses’ spontaneous sports activities is the basic countermeasure to improve this situation, for it can not only reduce the government economic investment, but also meet the needs of people’s physical exercises. This paper takes five parks – Sunrise Park, Temple of Heaven Park, Beihai Park, Yu Yuantan Park and Purple Bamboo Park – in Beijing city as research objects, and makes the research on the current developing situations of the organization of masses’ spontaneous sports activities. Based on BP neural network model, the paper makes analysis according to the masses’ weekly exercises frequency and duration in the above-mentioned five parks. The results show that the organization of masses’ spontaneous sports activities in Temple of Heaven Park and Beihai Park is going quite well, while the situation of Sunrise Park and Purple Bamboo Park is far from that good.

scholarly journals Credit of small and medium sized scientific and technological enterprises based on BP neural network Evaluation research

2021 ◽  
Vol 336 ◽  
pp. 09010
Author(s):  
Liulu Zhang ◽  
Xiao Zuo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Credit Rating ◽  
Evaluation Model ◽  
Science And Technology ◽  
Evaluation Research ◽  
Credit Evaluation ◽  
Neural Network Algorithm ◽  
Model Training

Aiming at the problem of credit evaluation of science and technology-based small and medium-sized enterprises in China, a credit evaluation system based on machine learning is proposed. A total of 17 indicators are selected from five aspects of solvency, profitability, operation ability, growth ability and R & D ability. Finally, 11 representative indicators are selected. Then through BP neural network algorithm to build a credit evaluation model, training and Simulation of the credit rating of science and technology-based SMEs. The results show that the evaluation model has good generalization ability, and can effectively evaluate the credit of science and technology-based SMEs.

scholarly journals Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Yuchao Zheng ◽  
Heng Zhong ◽  
Yong Fang ◽  
Wensheng Zhang ◽  
Kai Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Training Sample ◽  
Relational Theory ◽  
Entropy Method ◽  
Engineering Practice ◽  
Entropy Weight ◽  
Trained Neural Network ◽  
Grey Relational

A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

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