基于支持向量回归集成的陀螺仪参数漂移预测

Parameter Drift Forecasting of Gyro Based on Ensemble of Support Vector Regression

泛化能力是智能方法用于参数预测的最重要的问题之一,提出了支持向量回归集成方法。为了增加个体之间的差异性,提出了基于聚类方法的个体生成方法。首先利用聚类方法将样本分为若干子类,然后用不同结构的支持向量回归学习不同的样本子类,权值由个体在验证集上的泛化误差决定。将ESVR陀螺仪参数飘移数据的预测,并与单支持向量回归,单神经网络,神经网络集成以及组合预测方法进行比较。结果证实,ESVR的预测精度总体高于其他方法。

Generalization performance is one of the most important problems of intelligent approaches for parameters forecasting. This paper presents an ensemble of support vector regression （ESVR） which has better generalization performance than other intelligent approaches. To increase the diversity among individuals of ensemble, the paper proposes an individual generating approach based on clustering technique. Firstly, ESVR is used to classify training samples as several subclasses that are used to train different individuals with different kernel functions. The ensemble weights of individuals are determined by the generalization errors on the validation sets. ESVR is tested on the parameter drift data of gyroscope. By comparing single SVR, single neural network, neural network ensemble and combination approach with ESVR in generalization performance, the results reveal that ESVR has better generalization performance than other intelligent approaches in most cases.