Abstract: BP Neural Network is an effective method for prediction so far，but there are some self-determinations in its practical applications，so a new BP Neural Network based on improved Particle Swarm Optimization（PSO） is proposed.The capacity of solving nonlinear problems of this algorithm is enhanced effectively through adjusting inertia factor ω dynamically.At the same time，the convergence speed of this algorithm and the capacity of searching global extremum is increased.The improved BP Network model is built up，and the rainfall in a certain city is analyzed by this model and gradually regression method.Evidence shows this model has high accuracy and stability.

摘要： 目前BP神经网络是一种有效的预测方法，但在实际应用当中存在着一些自身的缺点，为此提出了一种基于改进粒子群算法的BP神经网络。通过动态调整粒子群算法中的惯性因子ω，有效地增强了算法对非线性问题的处理能力，同时提高了算法的收敛速度和搜索全局最优值的能力。建立改进后的BP网络模型，通过该模型和逐步回归方法对某市降水量进行实例分析。分析结果表明，改进后的BP网络模型具有较高的准备预报能力和稳定性。