期刊
NEUROCOMPUTING
卷 249, 期 -, 页码 72-85出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2017.03.023
关键词
Solar greenhouse; Support vector machine; Radial basis function; Convex bidirectional extreme learning machine
Predictions regarding the solar greenhouse temperature and humidity are important because they play a critical role in greenhouse cultivation. On account of this, it is important to set up a predictive model of temperature and humidity that would precisely predict the temperature and humidity, reducing potential financial losses. This paper presents a novel temperature and humidity prediction model based on convex bidirectional extreme learning machine (CB-ELM). Simulation results show that the convergence rate of the bidirectional extreme learning machine (B-ELM) can further be improved while retaining the same simplicity, by simply recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. The performance of the CB-ELM model is compared with other modeling approaches by applying it to predict solar greenhouse temperature and humidity. The experiment results show that the CB-ELM model predictions are more accurate than those of the B-ELM, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Radial Basis Function (RBF). Therefore, it can be considered as a suitable and effective method for predicting the solar greenhouse temperature and humidity. (C) 2017 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据