Dynamical systems and processes that either exhibit non-smooth behaviours (eg through
logic control or natural phenomena) or work in different modes of operation are usually …

​ Hybrid System Identification helps readers to build mathematical models of dynamical
systems switching between different operating modes, from their experimental observations …

This chapter introduces the main topic of the book, that is, hybrid system identification. It
details the various models of hybrid systems before posing the hybrid system identification …

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for
approximating complex dynamical models of biological wastewater treatment processes. We …

This paper proposes a one-step-ahead Bayesian output predictor for the linear stochastic
state space model with uniformly distributed state and output noises. A model with discrete …

The paper presents an optimal Bayesian transfer learning technique applied to a pair of
linear state-space processes driven by uniform state and observation noise processes …

In this work, a new two-stage identification method based on dynamic programming and
sparsity inducing is proposed for switched linear systems. Our method achieves sparsity …

In this paper, we propose an identification scheme for continuous-time switched linear
systems with input-output form. The identification problem of such system consists in the …

This study aims to determine how to deal with the identification from input and output data of
switched linear systems (SLSs) with Box and Jenkins models. The identification difficulties of …

We investigate sensor network nodes that sequentially infer states with bounded values, and
affected by noise that is also bounded. The transfer of knowledge between such nodes is the …