北华大学学报（自然科学版）2017，Vol.18Issue(4)：557-560,4.DOI:10.11713/j.issn.1009-4822.2017.04.029
参数优化支持向量机的农业大棚温室温度预测模型
Prediction Model on Agricultural Greenhouse Temperature Based on Support Vector Machine with Parameter Optimization
张晓丹1
作者信息
- 1. 北华大学电气信息工程学院,吉林 吉林 132021
- 折叠
摘要
Abstract
The paper constructs a prediction model on the temperature in the agricultural greenhouse based on kernel function linear,polynomial,radial basis function,sigmoid and the penalty parameters c and gamma values optimized by Particle Swarm Algorithm.The paper constructs the prediction model named as P_RBF model in which penalty parameters c is 14.392 and gamma value is 0.01 optimized by Particle Swarm Algorithm,on which the prediction accuracy of the train set consisted of 24 times,measured data is 90.849%.Based on P_RBF model,the prediction accuracy of the prediction set consisted of 5 times,measured data is 90.545%,which shows the model is robust.P_RBF model indicates that the prediction result is reliable on the temperature in the greenhouse and the temperature change trend may be accurately predicted,and solve the key factor on the greenhouse control being intelligent,which is difficult to predict the temperature.关键词
粒子群算法/支持向量机/农业大棚温室Key words
particle swarm algorithm/support vector machine/agricultural greenhouse分类
信息技术与安全科学引用本文复制引用
张晓丹..参数优化支持向量机的农业大棚温室温度预测模型[J].北华大学学报（自然科学版）,2017,18(4):557-560,4.
