The theory of variable structure systems (VSS) with sliding modes is currently one of the most significant research topics within the control engineering domain. Moreover, recently a number of important applications of the theory in the field of power electronics, motion control, robotics, bioprocess etc. have also been reported. Therefore, this paper presents a brief introduction to the theory...

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding mode concepts, form...

The main purpose of this paper is to study the lattice structure of variable precision rough sets. The notion of variation in precision of rough sets have been further extended to variable precision rough set with variable classification error and its algebraic properties are also studied.

Modern data-analysis methods are typically applicable to a single dataset. In particularly, they cannot integratively analyze datasets containing different, but overlapping, sets of variables. We show that by employing causal models instead of models based on the concept of association alone, it is possible to make additional interesting inferences by integrative analysis than by independent an...

State and parameter estimation techniques are important tools which provide accurate estimates of system states. This is important for the reliable and safe control of mechanical and electrical systems. Most estimation techniques are derived in discrete-time, due to the wide use of digital computers. However, continuous-time derivations do exist, and are particularly useful for studying estimat...

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms based on the EM and the Minimum Spanning Tree algorithms that learn mixtures of trees in the ML framework. The method can be extended to take into account priors and, for a wide class of priors that includes the D...