Local Gaussian Processes for Identifying Complex Mobile robot System
Xingguo Song, Zhongqing Cao, Hongli Gao · 2018
Nonparametric Gaussian processes regression (GPR) is an important tool in machine learning, can be applied in identifying nonlinear models from experimental data, especially, the prediction of mean and variance present the useful advantage. However, when dealing with the large number of training data for the prediction of a complex dynamics system, GPR is not suitable to implement in real-time learning systems. To reduce the computation effort, local learning algorithm is introduced to improve the global Gaussian processes (GP) model in this paper. In this paper, a convenient and effective method for building local model network is proposed and then local GP for weighted regression is performed. The proposed local GPR method is implemented on a simulated example of online identification and prediction fast for a complex dynamic system of wheeled mobile robot.