A new adaptive learning control approach is proposed for a class of nonlinearly parameterized complex dynamical networks with unknown time-varying parameters. By using the parameter separation and reparameterization technique, the adaptive learning laws of periodically timevarying and constant parameters and an adaptive control strategy are designed to ensure the asymptotic convergence of the s...

In this paper we address the problem of augmenting a linear observer with an adaptive element. The design of the adaptive element employs two nonlinearly parameterized neural networks, the input and output layer weights of which are adapted on line. The goal is to improve the performance of the linear observer when applied to a nonlinear system. The networks’ teaching signal is generated using ...

this paper studies the consensus problem of nonlinear leader-following multi-agent systems (mas). to do this, the error dynamics between the leader agent and follower ones are described via a takagi-sugeno (ts) fuzzy model. if the obtained ts fuzzy model is stable, then all of the nonlinear agents reach consensus. the consensus problem is investigated based on the parameterized or fuzzy lyapuno...

This paper is a survey of results presented in the recent book Gyllenberg and Silvestrov [GS08]. This book is devoted to studies of quasi-stationary phenomena for nonlinearly perturbed stochastic processes and systems. New methods of asymptotic analysis for nonlinearly perturbed stochastic processes based on asymptotic expansions for perturbed renewal equation and recurrence algorithms for cons...