We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical model...
We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matr...
Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hane...
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems with unknown steady state. First, the nonlinear system is id...
Hui Peng, Zi-Jiang Yang, Weihua Gui, Min Wu, Hideo...