Study on Neural Network Modeling of Greenhouse Environment Based on Partial Least Squares Algorithm

摘要：

The greenhouse environment model is an important basis to make control strategies and optimize control method. If correlation among multi-variables of greenhouse environmental model exists, the accuracy of the model decreases. In this paper, utilize partial least squares (PLS) to extract principal components of data, adopt radial basis function neural network (RBFNN) to construct control model of greenhouse environment in northern region of china. And this model is compared with the Orthogonal Least Square (OLS) algorithm in performance. The results indicate that RBF network model of the greenhouse environment based on PLS has smaller network structure, and is superior to OLS algorithm in the approximation ability and generalization ability. The model has laid a good foundation for designing control scheme and structure of greenhouse environment.

关键词： facility agriculture greenhouse environment model PLS

作者: Bin Zhao Keqi Wang

作者单位: College of Electromechanical Engineering Northeast Forestry University Harbin, eilongjiang Province, College of Electromechanical Engineering Northeast Forestry University Harbin, Heilongjiang Province

会议类型: 国际会议

会议名称: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

会议地点: 成都

会议语种:英文

页码: 89-92

在线出版日期: 2010-06-12（万方平台首次上网日期，不代表论文的发表时间）