{"title":"农村金融生态环境综合评价的BP神经网络模型","authors":"X. Ren","doi":"10.3233/JIFS-219085","DOIUrl":null,"url":null,"abstract":"For this reason, the key to solve the problem of rural financial development is to solve the problem of rural financial development. At present, the state has given important instructions to improve the rural financial ecological environment, but the relevant research on the evaluation of rural financial ecological environment in China is still insufficient. In view of this situation, this paper puts forward a BP neural network model for the comprehensive evaluation of rural financial ecological environment. First of all, this paper studies the relevant basic theory of financial ecology and ecological environment comprehensive evaluation. Through the research, this paper believes that the construction of rural financial ecological environment involves many factors, and each factor has a mutual influence. It is difficult to determine the influence of a single factor on the final result. Therefore, in view of this complex situation, this paper establishes a set of multi factor evaluation index systems including economy, policy, law, culture, etc. And these complex factors are trained by BP neural network. The training results were normalized to quantify the specific impact of each index on the rural financial ecological environment. Finally, in order to verify the actual evaluation effect of this model, a number of comparative experiments including validity verification, stability analysis, comparison and verification of different model error rates are carried out. Through the analysis of experimental data, we can see that the BP neural network evaluation model in this paper has good comprehensive performance and significantly improves the calculation accuracy compared with the traditional analytic hierarchy process.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BP neural network model for comprehensive evaluation of rural financial ecological environment\",\"authors\":\"X. Ren\",\"doi\":\"10.3233/JIFS-219085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For this reason, the key to solve the problem of rural financial development is to solve the problem of rural financial development. At present, the state has given important instructions to improve the rural financial ecological environment, but the relevant research on the evaluation of rural financial ecological environment in China is still insufficient. In view of this situation, this paper puts forward a BP neural network model for the comprehensive evaluation of rural financial ecological environment. First of all, this paper studies the relevant basic theory of financial ecology and ecological environment comprehensive evaluation. Through the research, this paper believes that the construction of rural financial ecological environment involves many factors, and each factor has a mutual influence. It is difficult to determine the influence of a single factor on the final result. Therefore, in view of this complex situation, this paper establishes a set of multi factor evaluation index systems including economy, policy, law, culture, etc. And these complex factors are trained by BP neural network. The training results were normalized to quantify the specific impact of each index on the rural financial ecological environment. Finally, in order to verify the actual evaluation effect of this model, a number of comparative experiments including validity verification, stability analysis, comparison and verification of different model error rates are carried out. Through the analysis of experimental data, we can see that the BP neural network evaluation model in this paper has good comprehensive performance and significantly improves the calculation accuracy compared with the traditional analytic hierarchy process.\",\"PeriodicalId\":44705,\"journal\":{\"name\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/JIFS-219085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fuzzy Logic and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JIFS-219085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
BP neural network model for comprehensive evaluation of rural financial ecological environment
For this reason, the key to solve the problem of rural financial development is to solve the problem of rural financial development. At present, the state has given important instructions to improve the rural financial ecological environment, but the relevant research on the evaluation of rural financial ecological environment in China is still insufficient. In view of this situation, this paper puts forward a BP neural network model for the comprehensive evaluation of rural financial ecological environment. First of all, this paper studies the relevant basic theory of financial ecology and ecological environment comprehensive evaluation. Through the research, this paper believes that the construction of rural financial ecological environment involves many factors, and each factor has a mutual influence. It is difficult to determine the influence of a single factor on the final result. Therefore, in view of this complex situation, this paper establishes a set of multi factor evaluation index systems including economy, policy, law, culture, etc. And these complex factors are trained by BP neural network. The training results were normalized to quantify the specific impact of each index on the rural financial ecological environment. Finally, in order to verify the actual evaluation effect of this model, a number of comparative experiments including validity verification, stability analysis, comparison and verification of different model error rates are carried out. Through the analysis of experimental data, we can see that the BP neural network evaluation model in this paper has good comprehensive performance and significantly improves the calculation accuracy compared with the traditional analytic hierarchy process.
期刊介绍:
The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.