Optimal Regulation Method of Greenhouse Light Environment considering Photosynthetic Rate Model Error

Due to the complexity of photosynthesis greenhouse crops, there are inevitable errors between established photosynthetic rate model and actual value, which makes it difficult guarantee optimality even feasibility optimal regulation light environment based on original model. To solve these problems, this paper proposes an method considering error The main contributions as follows: firstly, is by using compensation extreme learning machine reduce output value. Then, introduced into problem form after compensation, constrained adaptive particle swarm optimization algorithm used model, ensures rapid convergence results, results closer values, improves reliability environment. Finally, effectiveness proposed verified experiments. simulation also show that can give a reasonable intensity setting value in time under different temperature, humidity, CO2 conditions inside greenhouse, meet needs make growth crops efficient stable.