基于灰色小波核偏最小二乘的传感器网络数据预测融合研究

摘　要：针对如何降低传感器网络中采集的非平稳、非线性信号的数据传输量，提出了一种基于灰色Morlet小波核偏最小二乘(GMWKPLS)的预测融合模型。该模型把灰色模型预测的思想融入到核偏最小二乘(KPLS)中，采用构造的Morlet小波核函数进行数据变换，将输入映射到高维非线性的特征空间，在特征空间中，利用线性偏最小二乘方法构造预测融合模型。通过对齿轮箱断齿工况升速过程中的振动信号进行分析，结果表明，该模型使用滑动窗方法不断更新建模数据进行动态预测，预测精度高，可大大降低数据传输量，获得显著的节能收益。通过与灰色RBF核偏最小二乘(GRBFKPLS)和RBF核偏最小二乘(RBFKPLS)预测模型对比，GMWKPLS性能最佳，预测误差范围在±0.4%以内。

Data prediction and fusion in a sensor network based on grey wavelet kernel partial least squares

In order to reduce the amount of data of non-stationary and nonlinear signals collected in a sensor network,a grey Morlet wavelet kernel partial least squares(GMWKPLS) model was proposed.In this model,grey prediction theory was firstly introduced into kernel partial least squares(KPLS).Then,the input-output data were mapped to a nonlinear higher dimensional feature space with Morelt kernel transformation.Finally,a prediction and fusion model was constructed with linear partial least squares.Moreover,the mov...