关键词: SVR, 细菌觅食算法, 粒子群算法, 光合速率

Abstract: In order to solve the problem that SVR parameters affected the model performance, this paper put forward a hybrid particle swarm optimization algorithm (PSOA) based on chemotactic operation of bacteria foraging optimization algorithm (BFOA) to optimize the SVR penalty parameters and kernel parameters. At the same time, in order to achieve fine control of the greenhouse environment, combining with the growth factors of greenhouse crops, this paper established a regression model based on chemotactic-improved particle swarm optimization to predict the photosynthetic rate in greenhouse. Taking greenhouse tomato at seedling stage, flowering stage and fruiting stage as examples, this paper carried out experiments and compareison support vector regression (SVR) with support vector regression based on particle swarm optimization(PSO-SVR). The results showed that the determination coefficients of photosynthesis rate and the predicted value of the photosynthesis rate in these 3 growth stages were 0.954 8, 0.985 4 and 0.951 5, respectively, which were closer to 1 than the other two prediction models, indicating the prediction effects of constructed model were better and the proposed algorithm was valid. These results provided theoretical basis for precise controlling and regulating the greenhouse environment according to the needs of crop photosynthesis.

Key words: SVR, bacterial foraging algorithm, particle swarm optimization, photosynthesis rate