农村金融生态环境综合评价的BP神经网络模型

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
X. Ren
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引用次数: 0

摘要

因此,解决农村金融发展问题的关键在于解决农村金融发展问题。目前,国家对改善农村金融生态环境作出了重要指示,但中国农村金融生态环境评价的相关研究仍显不足。针对这种情况,本文提出了一种用于农村金融生态环境综合评价的BP神经网络模型。本文首先对金融生态学和生态环境综合评价的相关基础理论进行了研究。通过研究,本文认为农村金融生态环境建设涉及诸多因素,各因素之间存在着相互影响。很难确定单个因素对最终结果的影响。因此,针对这一复杂情况,本文建立了一套包括经济、政策、法律、文化等在内的多因素评价指标体系。并利用BP神经网络对这些复杂因素进行训练。将训练结果归一化,量化各指标对农村金融生态环境的具体影响。最后,为了验证该模型的实际评价效果,进行了有效性验证、稳定性分析、不同模型错误率的对比验证等多项对比实验。通过对实验数据的分析可以看出,本文建立的BP神经网络评价模型具有较好的综合性能,与传统的层次分析法相比,计算精度显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
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.
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