Abstract
A genetic algorithm is used to optimize the weights and thresholds of the BP neural network in this paper, this method improves the disadvantages of the BP neural network. For example, It is insensitive to weights and thresholds. In addition, it has a slow convergence rate and is prone to fall into local minima. etc. To verify the improvement effect, the BP neural networks and the improved BP neural networks were used to simulate the weather in Rizhao city, Shandong Province, respectively. The results of simulation show that the GA-BP neural network model is superior to BP neural network model in the accuracy of temperature prediction in long-term weather. Next, the GA-BP neural network was further optimized by selecting the optimal number of hidden layers and changing the impact factors, and the prediction results were fitted. The analysis shows that the accuracy of the improved GA-BP model in prediction is higher.
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