@Article{phyton.2022.021096,
AUTHOR = {Wenbin Dai, Lina Wang, Binrui Wang, Xiaohong Cui, Xue Li},
TITLE = {Research on WNN Greenhouse Temperature Prediction Method Based on GA},
JOURNAL = {Phyton-International Journal of Experimental Botany},
VOLUME = {91},
YEAR = {2022},
NUMBER = {10},
PAGES = {2283--2296},
URL = {http://www.techscience.com/phyton/v91n10/48005},
ISSN = {1851-5657},
ABSTRACT = {Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data
to model greenhouses, but there is a widening gap in the application of different neural networks. This paper
proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm
(GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function,
wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with
back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term
greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally
optimize the initial weights of the original model, which improved the insensitivity of the model to the initial
weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results
for the greenhouse showed that the model training speed, prediction results accuracy and model stability of
the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN
in the greenhouse.},
DOI = {10.32604/phyton.2022.021096}
}