改进粒子群优化-Elman算法在发动机曲轴脉宽预测中的应用

针对发动机曲轴脉宽难以预测的问题，提出了改进粒子群(PSO)优化Elman神经网络预测的方法。采用Elman神经网络建立脉宽预测模型，根据网络陷入局部最优的代数与迭代次数动态更新网络惯性权重使PSO算法得到改进，利用改进的PSO算法对Elman神经网络的权值和阈值进行优化。对YC6G270-30型增压中冷柴油机曲轴信号脉宽的预测结果表明，改进的PSO-Elman算法比最小二乘、Elman、PSO-Elman算法具有更高的预测精度，收敛速度更快，验证了所提出方法的有效性与实用性。

Applications of Advanced PSO-Elman in Engine Crankshaft Pulse Width Predictions

Aimed at the unpredictability of the engine crankshaft pulse widths, advanced PSO-Elman predictive method was put forward. The model of pulse width predictions was built by Elman neural network, according to the generation of network trapped into the local optimums and the iterations, the inertia weight were updated and the PSO was improved. The Elman weight and threshold were optimized by advanced PSO. Compared with the least squares, Elman and PSO-Elman by predicting the YC6G270-30 crankshaft pulse widths, the advanced PSO has simple structures and fast convergences. At the same time, the validity and practicability of the proposed method were verified.