SIAM Journal on Scientific and Statistical Computing
Guaranteed properties of gain scheduled control for linear parameter-varying plants
Automatica (Journal of IFAC)
The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Robust and optimal control
Matrix computations (3rd ed.)
Subspace identification of multivariable linear parameter-varying systems
Automatica (Journal of IFAC)
Least squares support vector machine based partially linear model identification
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Environmental Modelling & Software
Hi-index | 22.14 |
Subspace identification methods for multivariable linear parameter-varying (LPV) and bilinear state-space systems perform computations with data matrices of which the number of rows grows exponentially with the order of the system. Even for relatively low-order systems with only a few inputs and outputs, the amount of memory required to store these data matrices exceeds the limits of what is currently available on the average desktop computer. This severely limits the applicability of the methods. In this paper, we present kernel methods for subspace identification performing computations with kernel matrices that have much smaller dimensions than the data matrices used in the original LPV and bilinear subspace identification methods. We also describe the integration of regularization in these kernel methods and show the relation with least-squares support vector machines. Regularization is an important tool to balance the bias and variance errors. We compare different regularization strategies in a simulation study.