基于增量稀疏核极限学习机的发动机状态在线预测

针对发动机状态在线预测中样本累积、预测模型膨胀和在线更新速度慢等问题，提出了基于增量稀疏核极限学习机的在线预测方法.该方法定义了KELM核函数矩阵的稀疏测量矩阵，并根据矩阵原子相干最小化和自信息量最大化的样本信息度量准则实现在线样本前向稀疏与后向删减，提高了样本稀疏化效率.利用有效样本对测量矩阵在最佳阶数内进行在线扩充与修剪，限制了预测模型膨胀.利用改进的增量建模方法对模型的核权重矩阵进行递推更新，从而建立规模有限且结构稀疏的在线预测模型，提高了在线建模速度.仿真数据和发动机状态参数在线预测实验结果表明，与现有在线预测方法相比，ISKELM具有更高的样本稀疏化和在线建模效率.对发动机排气温度进行120步预测时，预测速度分别提高了80.50%和31.72%，预测精度分别提高了48.56%和15.81%. 

Engine Condition Online Prediction Based on Incremental Sparse Kernel Extreme Learning Machine

Aiming at the problems of sample accumulation, model inflation and slow online updating speed in engine condition online prediction process, an online prediction method based on incremental sparse kernel extreme learning machine(ISKELM)was proposed. Firstly,a sparse measurement matrix was defined for the kernel function matrix of KELM, and the operations of forward sparseness and backward deletion for large-scale samples were performed according to the principle of sample information measurement consisting of coherence minimization and self-information maximization. It improves the efficiency of sample sparseness. Then the sparse measurement matrix was expanded and pruned online by using the effective samples under the best dictionary order, which limited the model inflation. Lastly, the kernel weight matrix of the model was updated in a recursive way through the improved incremental modeling method. So an online learning model of ISKELM with a limited order and sparse structure was established to distinctly improve the online modeling speed. The online prediction experimental results with simulation data and engine condition parameters show that, compared with two existing online prediction methods, ISKELM has higher efficiency of sample sparseness and online modeling. When the engine exhaust temperature is predicted by 120 steps, the prediction speed is increased by 80.50% and 31.72% respectively, and the prediction accuracy is improved by 48.56% and 15.81% respectively.