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基于智能农业中PSO-LSTM模型的集体经济发展预测

Chunwu Zheng¹, Huwei Li¹

¹Henan Economy and Trade Vocational College, Zhengzhou, China.

PeerJ. Computer science

|June 22, 2023

概括

本研究介绍了一种粒子群优化-长期短期记忆 (PSO-LSTM) 模型,用于预测集体农业经济 (CAE). 与传统方法相比,先进的PSO-LSTM模型为智能农业开发提供了更高的准确性.

科学领域:

农业经济学农业经济学
人工智能的人工智能
数据科学数据科学数据科学

背景情况:

集体农业经济 (CAE) 面临着由于天气和政策等非线性和不确定因素的挑战.
传统的统计回归模型在CAE的预测准确性和概括性方面存在局限性.
智能农业,利用物联网和人工智能,旨在加强资源协调和市场准入,以实现可持续的CAE发展.

研究的目的:

为集体农业经济 (CAE) 提出先进的生产预测方法.
提高对非线性和不确定的CAE数据的预测准确度和概括能力.
为CAE的可持续发展提供一个强大的数据驱动的参考.

主要方法:

利用长短期记忆 (LSTM) 网络,一种深度循环神经网络,用于区域CAE预测.
采用粒子集群优化 (PSO) 算法来优化LSTM模型参数,以提高全球准确性.
将拟议的PSO-LSTM模型与标准LSTM和传统机器学习方法的性能进行了比较.

主要成果:

与未经优化的LSTM模型相比,PSO-LSTM模型表现出卓越的性能.
拟议的方法在CAE预测方面显著优于传统的机器学习方法.
评估指标如根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 证实了PSO-LSTM的提高准确性.

结论:

该PSO-LSTM模型提供了一个更准确,更可靠的方法来预测CAE.
这种方法提供了有价值的数据参考,以支持集体农业经济的高质量和可持续发展.
人工智能和优化算法的集成对于解决智能农业经济学的复杂性至关重要.

关键词:

这是LSTM的LSTM.粒子群集优化优化粒子群集优化生产预测生产预测.经常性的神经网络.智能农业 - 智能农业

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