Abstract-- This paper focuses on the identification of nonlinear hybrid systems involving unknown nonlinear dynamics. The proposed method extends the framework of [1] by introducin...
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem b...
This paper studies a method for the identification of Hammerstein models based on Least Squares Support Vector Machines (LS-SVMs). The technique allows for the determination of th...
Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Su...
A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (...
The Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent parame...