A Novel Agricultural Engineering Trend Prediction based on Neural Network Prediction Model with the Rough Set Theory

Abstract

This paper proposes the novel agricultural engineering trend prediction based on neural network prediction model with the rough set theory. The relationship between multiple decision makers may be independent of each other, and therefore need to use approximate more than two yuan to the target, this paper proposes the concept of multi-granulation rough set, in multi granularity in rough set, with two and two above the indiscernibility relation of the concept of approximation and analyzes the multi-granulation rough set and the relationship between classical rough set. Agricultural engineering is the relationship between agricultural research organisms and engineering means of core interaction, resulting in formation and basic theory of agricultural engineering, for the rational development and utilization of agricultural resources improve the environment factors of the agricultural production process and promote the service of agricultural modernization. This paper provides the new idea of dealing with the issue that will promote the further development of the corresponding research.

Performance Evaluation Model of Agricultural Enterprise Technology Innovation Based on GA-BP Neural Network

“…Different from general enterprise performance evaluation, in the evaluation of main financial indicators, technological innovation performance evaluation has certain particularity. Du [ 13 ] first pointed out that priority should be given to improving inventory turnover rate and accounts receivable turnover rate. The weights of indicators that fit with the characteristics of agriculture are adapted to the characteristics of agriculture.…”

“…Li et al [ 23 ] conducted research on private enterprises in my country, established a performance evaluation index system including enterprise objectives, partnership, and internal process, and combined neural network and dynamic fuzzy method to demonstrate the evaluation system. Other scholars such as Du [ 13 , 14 ] applied the BP neural network model to evaluate the application.…”