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The Research And Construction Of DEA-BP Neural Network Combinatorial Model For The Recognition Of Regional Core Competitiveness

Posted on:2009-05-06Degree:MasterType:Thesis
Country:ChinaCandidate:W L SongFull Text:PDF
GTID:2189360245487788Subject:Quantitative Economics
Abstract/Summary:PDF Full Text Request
Based on in-depth study of the existing outcome ,this paper introduces the Econometric methods into the study of the recognition of the regional core competitiveness, and makes a bold innovation to proposes a new method on recognizing regional core competitiveness——DEA-BP combinatorial model ,which is based on the regional core competitiveness theory , data envelopment analysis and neural network analysis methods. This research redefines the concept and content of regional core competitiveness , focuses on quantitative methods to recognize the regional core competitiveness , improves the life cycle identification technology of the regional core competitiveness, establishes the index system, and uses DEA-BP combinatorial model on the study of recognition of regional core competitiveness in coastal areas.The first part "Introduction" exposes on the issue of background, significance of this issue, and sums up methods of the recognition of regional core competitiveness on the literature, it also puts forward its ideas and framework.The second part "Basic study on regional core competitiveness" proposes the concept of regional core competitiveness based on ability theory, after a systematic study on regional core competitiveness. Then it further expounds on the differences between regional core competitiveness and the relative concepts, its characteristics and the formation and evolution rule.The third part "the research and construction of the recognition of regional core competitiveness model" in-depth studies the quantitative model to identify the regional core competitiveness. By study on how DEA and BP can be used on the recognition of regional core competitiveness, and according to there limitation in using, this part put forward a new method on the recognition of regional core competitiveness——DEA-BP combinatorial model, and then steps to the implementation of a detailed analysis, and sums up the application skills of the combinatorial model to enhance the efficiency of the model solution.Part IV "DEA-BP combinatorial model Index System Construction" bases on the principle of scientific, comprehensive, independence and measurable, this paper constructs an Index System according to the basis of the regional core competitiveness theory, applicable to DEA-BP combinatorial model, which includes 15 indicators for evaluation that belongs to input factors and output factors.Part V"An Empirical Study on the Recognition of coastal areas core competitiveness based on DEA-BP combinatorial model"makes a empirical testing on the application of the DEA-BP combinatorial model to recognize the regional core competitiveness, by using 11 coastal provinces and cities'actual data. After anglicizing results of the model, it gives a comprehensive evaluation of coastal areas in the regional core competitiveness situation.Part VI "conclusions and Development" gives the conclusions and the development direction. According to the analysis of the results, it is found that DEA-BP combinatorial model can be a good way to make up the original lack of the former method. Contrast with the other studies ,it is sure that DEA-BP combinatorial model can be successfully achieved on the recognition of regional core competitiveness, and the results and qualitative analysis results are consistent. And because the combinatorial model takes environmental depletion, the administrative efficiency, and quality of life of residents, the level of opening up Evaluation indicators into consider, this paper brings a new perspective on the evaluation of regional development. At the same time, the paper pointed out its innovation and inadequate, and the direction and content need for further study in the future.
Keywords/Search Tags:Regional core competitiveness, recognition, DEA, BP neural network, combinatorial model
PDF Full Text Request
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