The BP network has the disadvantages such as the low learning efficiency, slow convergence, easily falling into local minimum state, and poor adaptability. Particle swarm optimization (PSO) algorithm is fast in convergence especially at the initial stage, simple in the computing and easy to implement. And when compared with the genetic algorithms, it has no complex operations of hybrid codecs or mutation, which proves to be a good optimization algorithm. However, particle swarm optimization (PSO) algorithm also has disadvantages. It is slower in convergence rate at the late evolution of the algorithm. In this paper, a new BP Neural Network based on improved Particle Swarm Optimization(PSO)is proposed. The convergence speed of this algorithm and the capacity of searching global optimum is increased through adjusting the adaptability of learning factor. The simulation results illustrate that the improved PSO is superior to the standard BP algorithm and particle swarm optimization.%由于BP网络存在学习效率低、收敛速度慢、易陷入局部极小状态、适应能力较差等缺点，而粒子群优化（PSO）算法的收敛速度快（尤其是在进化初始阶段），运算简单、易于实现，又没有遗传算法的编解码和杂交、变异等复杂运算，因此是一种很好的优化算法。但是，PSO算法也存在不足，该算法进化后期存在速度变慢以及早熟的现象。提出一种改进的粒子群BP神经网络对高炉炉温进行预测。通过调整粒子群算法中学习因子的自适应能力，提高算法的收敛速度和搜索全局最优的能力。通过仿真结果说明改进的粒子群算法要优于BP算法和标准的粒子群算法。
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