Research on WNN Greenhouse Temperature Prediction Method Based on GA

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 datato model greenhouses, but there is a widening gap in the application of different neural networks. This paperproposes 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 withback propagation neural networks (BPNN), which was conducive to the prediction and control of short-termgreenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globallyoptimize the initial weights of the original model, which improved the insensitivity of the model to the initialweights and thresholds, and improved the training speed and stability of the model. Finally, simulation resultsfor the greenhouse showed that the model training speed, prediction results accuracy and model stability ofthe GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNNin the greenhouse.