The BP Neural Network Optimizing Design Model for Agricultural Information Measurement Based on Multistage Dynamic Fuzzy Evaluation

Zhibin Liu, Li Bai
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引用次数: 1

Abstract

The agricultural information level is on the initial stage in China, so we should pay more attention to its construction, but how to measure the agricultural information degree is a major issue. This paper overcomes the shortcoming of traditional linear agricultural information degree evaluation method, proposes a BP neural network evaluating method based on the multistage dynamic fuzzy judgment, takes the multistage dynamic fuzzy judgment as the sampling foundation, uses the BP neural network principle to establish evaluation model. This method not only can exert the unique advantages ofBP neural network, but also overcome the difficulty of seeking the high grade training sample data. The agricultural information degree evaluation of 10 cities in Jilin province indicates that the method to evaluate the agricultural information degree is stable and reliable.
基于多阶段动态模糊评价的BP神经网络农业信息测量优化设计模型
中国农业信息化水平尚处于初级阶段,应重视农业信息化水平的建设,但如何衡量农业信息化程度是一个重大问题。本文克服了传统线性农业信息程度评价方法的不足,提出了一种基于多阶段动态模糊判断的BP神经网络评价方法,以多阶段动态模糊判断为抽样基础,利用BP神经网络原理建立评价模型。该方法既能发挥bp神经网络的独特优势,又克服了寻找高质量训练样本数据的困难。通过对吉林省10个地市的农业信息化程度评价表明,该评价方法是稳定可靠的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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