基于PCA-PSO-LSSVM的温室大棚温度预测方法

Temperature prediction method of greenhouse based on PCA-PSO-LSSVM

针对温度预测的精度和效率问题,提出了在主成分分析(PCA)法的条件下,利用粒子群优化(PSO)算法优化最小二乘支持向量机(LSSVM)的温室大棚温度预测方法。采用PSO算法对LSSVM的模型参数进行优化,并以自动获取的最佳参数组合构建温度与其影响因子间非线性预测模型。利用搭建的温室大棚智能监控系统对人工温室中的6种环境参数进行采集,并利用所测数据对上述模型进行验证。实验结果表明:与PCA-LSSVM预测模型和PSO-LSSVM预测模型相比,所提预测模型预测效果良好。3种模型评价指标均优于其他预测方法。基于PCA-PSO-LSSVM温度预测模型在全局优化及收敛速度方面具有较大优势,具有良好的自学能力和自适应能力,预测精度高。

In order to improve precision and efficiency of temperature prediction, propose a new temperature prediction method for greenhouse, which uses particle swarm optimization （ PSO ） algorithm to optimize the least squares support vector machine（LSSVM） under the condition of principal component analysis （PCA）. The model parameters of the LSSVM are optimized by PSO algorithm, and the optimal parameters combination is used to construct the nonlinear prediction model for temperature and its impact factors. The six environmental parameters in the artificial greenhouse are collected by using the greenhouse intelligent monitoring system, and the above model is validated by the measured data. The experimental results show that, compared with the prediction models of PCA-LSSVM,and PSO-LSSVM,the proposed prediction model is the best under the same condition. The three models evaluation indexs are superior to other prediction methods. The temperature prediction model based on the PCA-PSO-LSSVM has great advantages in global optimization, convergence speed, self-learning ability, self- adaptability, and has high prediction precision.