Research on Grain Yield Prediction Method Based on Improved PSO-BP

Liguo Zhang, Jiangtao Liu, Lifu Zhi


Aimed at the highly nonlinear and uncertainty of grain yield changes, a new method for grain yield prediction based on improved PSO-BP is proposed. By introducing mutation operation and adaptive adjust of inertia weight, the problem of easy to fall into local optimum, premature, low precision and low later iteration efficiency of PSO are solved. By using the improved PSO to optimize BP neural network’s parameters, the learning rate and optimization capability of conventional BP are effectively improved. The simulation results of grain production prediction show that the predict accuracy of the new method is significantly higher than that of conventional BP neural network method, and the method is effective and feasible.

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