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- W1993969629 abstract "The greenhouse environment is an uncertain nonlinear system which classical modeling methods cannot solve. Support vector machines regression (SVMR) is well supported by mathematical theory and has a simple structure, good generalization ability, and nonlinear modeling properties. Therefore, SVMR offers a very competent method for modeling the greenhouse environment. However, to deal with uncertainty, the model must be rectified online, and Online Sparse Least-Squares Support Vector Machines Regression (OS_LSSVMR) was developed to solve this problem. OS_LSSVMR reduced the number of training samples through use of a sample dictionary, and consequently LSSVMR has sparse solutions; the training samples were added sequentially, so that OS_LSSVMR has online learning capability. A simplified greenhouse model, in which only greenhouse internal and external air temperatures were considered, was presented, after analyzing the factors in the greenhouse environment. Then the OS_LSSVMR greenhouse model was constructed using real-world data. The resulting model shows a promising performance in the greenhouse environment, with potential improvements if a more complete data setup is used." @default.
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- W1993969629 date "2009-04-01" @default.
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- W1993969629 title "Support vector machines regression and modeling of greenhouse environment" @default.
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- W1993969629 doi "https://doi.org/10.1016/j.compag.2008.12.004" @default.
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