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Adaptation in Dynamical Systems Hardcover – 28 Feb 2011. Ivan Tyukin is an RCUK Academic Fellow in the Department of Mathematics, University of Leicester. by. Ivan Tyukin (Author).

Cambridge Core - Nonlinear Science and Fluid Dynamics - Adaptation in Dynamical Systems - by Ivan Tyukin. Fast social-like learning of complex behaviors based on motor motifs.

Adaptation in Nonlinear Dynamical Systems (Adaptatsiya v nelineinykh dinamicheskikh sistemakh). Adaptation algorithms in finite form for nonlinear dynamic objects. Automation and Remote Control 64 (6), 951-974, 2003. IY Tyukin, VA Terekhov. Semi-passivity and synchronization of diffusively coupled neuronal oscillators. E Steur, I Tyukin, H Nijmeijer. Physica D: Nonlinear Phenomena 238 (21), 2119-2128, 2009. Adaptive Control in Technical Sytems (Adaptivnoe Upravlenie v Tekhnicheskikh Systemakh). VN Antonov, VA Terekhov, IY Tyukin. СП. Издательство . Петербургского университета, 2001.

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Ivan Tyukin Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions.

A class of adaptation algorithms for adaptive control of nonlinear dynamic objects of a class is introduced. Implementation of algorithms without the need for direct measurements of the first time derivatives is investigated and sufficient conditions for the existence of such algorithms are formulated. Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Here, we relate an adaptive property of a sensory system directly to its function as a carrier of information about input signals.

Adaptation in Dynamical Systems: Index. Part I. Introduction and Preliminaries: 1. Introduction 2. Preliminaries 3. The problem of adaptation in dynamical systems Part II. Theory: 4. Input-output analysis of uncertain dynamical systems . (More).

Ivan Tyukin; Cees van Leeuwen. We propose a technique for the design and analysis of decentralized adaptation algorithms in interconnected dynamical systems. Our technique does not require Lyapunov stability of the target dynamics and allows nonlinearly parameterized uncertainties. We show that for the considered class of systems, conditions for reaching the control goals can be formulated in terms of the nonlinear L 2-gains of target dynamics of each interconnected subsystem. Equations for decentralized controllers and corresponding adaptation algorithms are also explicitly provided.