基于改进粒子群算法的BP神经网络模型研究

BP Neural Network Model Research Based on Improved Particle Swarm Optimization Algorithm

为解决BP神经网络局部性收敛度慢的问题,提出了基于改进粒子群算法的BP神经网络模型.该方法通过粒子群进化速率动态调整惯性权重因子,提高了算法的收敛速度和全局搜索最优值的能力.提出的模型和改进的算法模拟仿真表明:该方法对收敛速度和精度有更好的拟合性.

To solve local convergence ot slow problems ot Bt＂ neural network, a tit＂ neural network model based on improved particle swarm optimization （PSO） algorithm was proposed. The convergence speed and theability of optimal value global searching were promoted by evolutionary rate adaptive inertia weight factor. Final- ly, the fitness of convergence speed and accuracy was validated by practical application through simulating the model and the improved algorithm.