基于粒子群优化BP神经网络的台风灾损预测模型研究

Typhoon Disaster Loss Forecasting Model Based on Particle Swarm Optimization and BP Neural Network

在对浙江省台风灾情实际情况研究的基础上,选择台风路径预报平均误差、预警能力指数等16个因子作为输入量,用粒子群算法(PSO)对BP神经网络的参数进行优化,以避免陷入局部极小和初始参数的抉择问题,提高模型的精度和收敛速度,从而建立了PSO-BP模型,并将模型应用于浙江台风灾情的预测。最后,对PSO-BP网络模型与普通BP模型的台风灾情预测训练效果进行对比,使用三个台风数据测试已训练好的网络,模型的预测结果显示,PSO-BP算法相较于BP算法有更高的精度,能较好的反应历年台风灾情的基本趋势。

Based on the research of typhoon disaster condition of Zhejiang province, the average error of typhoon track forecasting and pre-warning index, ete are used as inputs of model. Applying the particle swarm optimization algorithm to BP neural network is to avoid local minimum problem of BP and initial parameter problem and to improve precision of BP neural network and the convergence speed. The PSO-BP is established to forecast the ty- phoon disaster. The training results between PSO-BP and common BP are compared. Predicting outcomes of three typhoons showed PSO-BP had higher forecasting accuracy, and displayed the basic trend of typhoon of disaster better.