CAJ | 학술논문

针对于BP神经网络预测模型，收敛速度慢，精度较低，容易陷入局部极小值等缺点，提出了一种改进粒子群优化BP神经网络预测模型的算法。在该算法中，粒子群采用改进自适应惯性权重和改进自适应加速因子优化BP神经网络预测模型的初始权值和阈值，然后训练BP神经网络预测模型并预测。将该算法应用到几个典型的混沌时间序列预测。实验结果表明，该算法明显提高BP神经网络预测模型的收敛速度和预测模型的精度，减少陷入局部极小的可能。
침대우BP신경망락예측모형，수렴속도만，정도교저，용역함입국부겁소치등결점，제출료일충개진입자군우화BP신경망락예측모형적산법。재해산법중，입자군채용개진자괄응관성권중화개진자괄응가속인자우화BP신경망락예측모형적초시권치화역치，연후훈련BP신경망락예측모형병예측。장해산법응용도궤개전형적혼돈시간서렬예측。실험결과표명，해산법명현제고BP신경망락예측모형적수렴속도화예측모형적정도，감소함입국부겁소적가능。
BP neural network for forecasting has low speed of convergence, low precision and easily falling into the local minimum state. An improved prediction method of optimized BP neural network based on Improved Particle Swarm Opti-mization algorithm(IPSO)is proposed. The IPSO algorithm adopts modified adaptive inertia weight and adaptive acceler-ation coefficients to optimize the weights and thresholds of BP neural network. Then BP neural network is trained to search for the optimal solution. This experiment is done with several typical nonlinear systems. The results demonstrate that the improved method has faster convergence speed, higher accuracy and not easily falling into the local minimum state.