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基于 ANN 的土壤微生物共生网络构建方法。

Construction Means of Soil Microbial Synusiologic Network Based on ANN.

机构信息

College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China.

Shanxi Academy of Agricultural Science, Shanxi Agricultural University, Taiyuan, Shanxi 030031, China.

出版信息

Comput Intell Neurosci. 2022 Oct 3;2022:1708350. doi: 10.1155/2022/1708350. eCollection 2022.

Abstract

With the construction of synusiologic civilization and synusiologic environmental protection entering a new era driven by data, the breadth and depth of application of the DM technique in the domain of synusiologic environmental protection are constantly strengthened. If reasonable planning is not carried out in the process of social construction, it will cause unpredictable damage to the synusiologic environment. However, traditional synusiologic planning means too much human interference, and there are still some shortcomings in accuracy and operability, which means they cannot guide synusiologic construction well. In order to analyze the contribution of the soil nutrient data to soil fertility and dig out the knowledge describing soil fertility, this paper studies the construction means of soil microbial synusiologic network by ANN. By simulating the learning, memorizing, and processing problems of human brain neurons, the artificial network establishes a parallel distributed processing system computing DMG model with a large number of connections, which can quickly acquire knowledge from the outside world and store and process it and respond to the changes in the external environment in time. According to the research in this paper, the network performance of this algorithm is 18% better than that of the traditional algorithm, and it is suitable to be widely put into practice.

摘要

随着数据驱动的协同文明和协同环保建设进入新时代,DM 技术在协同环保领域的应用广度和深度不断加强。如果在社会建设过程中没有进行合理规划,将会对协同生态环境造成不可预测的破坏。然而,传统的协同规划手段受到过多的人为干扰,在准确性和可操作性方面仍存在一些不足,这意味着它们无法很好地指导协同建设。为了分析土壤养分数据对土壤肥力的贡献,并挖掘描述土壤肥力的知识,本文通过人工神经网络(ANN)研究了土壤微生物协同网络的构建方法。通过模拟人类大脑神经元的学习、记忆和处理问题,人工神经网络建立了一个具有大量连接的并行分布式处理系统计算 DMG 模型,能够快速从外部世界获取知识,并存储和处理知识,及时响应外部环境的变化。根据本文的研究,该算法的网络性能比传统算法好 18%,非常适合广泛应用于实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2740/9550418/8d5b26a0a1b2/CIN2022-1708350.001.jpg

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