基于组合神经网络的柴油机振动信号预测

根据柴油机振动信号的特性，使其在相空间里重构，再应用组合神经网络，对柴油机振动信号进行拟合和预测。该组合神经网络是一个两级系统，第一级有两个神经网络的预报——一个多目标前馈网络和一个函数耦合神经网络，用模糊反向传播算法进行训练；第二级是由第一级产生的两个预测结果混合得到的组合模型，采用Karmarkar的线性规划算法进行训练。实际应用证明了该方法的有效性。

DIESEL ENGINE VIBRATION SIGNAL PREDICTION BASED ON THE COMBINATION NEURAL NETWORKS

The vibration signals of the diesel engine are reconstructed in the phase space. Then the combination of neural networks to vibration signals approximation and time series prediction are applied. It is a two-stage system. The first stage contains two NN predictions, a multi-layer feed for work NN and functional link NN. They are trained by fuzzy back propagations algorithm. The second stage consists of a combination module to mix the two individual prediction proved in the first stage. It is trained by Karmarkar's linear programming algorithm .It is proved available in the example.