In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification metho...
This paper introduces a new general methodology for the modeling and reliability evaluation of small isolated power systems, which include wind turbines, photovoltaics, and diesel ...
Yiannis A. Katsigiannis, Pavlos S. Georgilakis, Ge...
This paper presents two contributions: A set of routines that manipulate instances of stochastic programming problems in order to make them more amenable for different solution ap...
A hierarchical least squares (HLS) algorithm is derived in details for identifying MIMO ARX-like systems based on the hierarchical identification principle. It is shown that the ...