基于ISSA-LSSVM的猪舍温度预测模型

IF 3.3 2区 农林科学 Q1 AGRONOMY
Yuqi Zhang, Weijian Zhang, Chengxuan Wu, Fengwu Zhu, Zhida Li
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引用次数: 0

摘要

猪圈内部的温度对猪有很大的影响。将猪圈内的温度控制在一定的范围内是环境控制中一个迫切需要解决的问题。目前猪圈温度的调节方法主要是手动和简单的自动控制。很少有智能控制,这种直接的方法存在控制精度低、能耗高、不及时性等问题,容易导致热应激情况的发生。为此,本文提出了一种基于多策略改进的麻雀搜索算法(ISSA),对最小二乘支持向量机(LSSVM)进行优化,形成猪舍温度预测模型。在麻雀搜索算法(SSA)的优化过程中,首先利用逆优点集生成麻雀种群的初始位置;其次,提出种群数更新公式,根据迭代次数自动调整发现者和关注者的数量,提高算法的搜索能力;最后,将自适应t分布应用于发现者位置变化,以细化发现者群体,进一步提高算法的搜索能力。使用23个基准函数进行了测试,结果表明ISSA优于SSA。通过与四种标准算法优化后的LSSVM模型进行比较,检验了ISSA-LSSVM模型的预测效果。最终,ISSA-LSSVM温度预测模型的MSE为0.0766,MAE为0.2105,R2为0.9818。结果表明,该预测模型具有较好的预测性能和预测精度,可为猪圈内部温度的预测和控制提供准确的数据支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction Model of Pigsty Temperature Based on ISSA-LSSVM
The internal temperature of the pigsty has a great impact on the pigs. Keeping the temperature in the pigsty within a certain range is a pressing problem in environmental control. The current pigsty temperature regulation method is based mainly on manual and simple automatic control. There is rarely intelligent control, and such direct methods have problems such as low control accuracy, high energy consumption and untimeliness, which can easily lead to the occurrence of heat stress conditions. Therefore, this paper proposed an improved sparrow search algorithm (ISSA) based on a multi-strategy improvement to optimize the least squares support vector machine (LSSVM) to form a pigsty temperature prediction model. In the optimization process of the sparrow search algorithm (SSA), the initial position of the sparrow population was first generated by using the reverse good point set; secondly, the population number update formula was proposed to automatically adjust the number of discoverers and followers based on the number of iterations to improve the search ability of the algorithm; finally, the adaptive t-distribution was applied to the discoverer position variation to refine the discoverer population and further improve the search ability of the algorithm. Tests were conducted using 23 benchmark functions, and the results showed that ISSA outperformed SSA. By comparing it with the LSSVM models optimized by four standard algorithms, the prediction effect of the ISSA-LSSVM model was tested. In the end, the ISSA-LSSVM temperature prediction model had MSE of 0.0766, MAE of 0.2105, and R2 of 0.9818. The results showed that the proposed prediction model had the best prediction performance and prediction accuracy, and can provide accurate data support for the prediction and control of the internal temperature of the pigsty.
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来源期刊
Agriculture-Basel
Agriculture-Basel Agricultural and Biological Sciences-Food Science
CiteScore
4.90
自引率
13.90%
发文量
1793
审稿时长
11 weeks
期刊介绍: Agriculture (ISSN 2077-0472) is an international and cross-disciplinary scholarly and scientific open access journal on the science of cultivating the soil, growing, harvesting crops, and raising livestock. We will aim to look at production, processing, marketing and use of foods, fibers, plants and animals. The journal Agriculturewill publish reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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