Sign In. Access provided by: anon Sign Out. Deterministic Learning and Rapid Dynamical Pattern Recognition of Discrete-Time Systems Abstract: Recently, a deterministic learning theory was proposed for identification and rapid pattern recognition of uncertain nonlinear dynamical systems.https://geochicgayzasim.gq/mejor-web-de-citas-casuales.php
Survivability of Deterministic Dynamical Systems
In this paper, we investigate deterministic learning of discrete-time nonlinear systems. For periodic or recurrent dynamical patterns, the persistent excitation PE condition can be satisfied by a regression subvector constructed from the neurons near the sequence. With the satisfaction of the PE condition, it is shown that the internal dynamics of an uncertain discrete-time nonlinear system can be accurately learned along the state sequence.
Using the learned knowledge, a rapid pattern recognition mechanism can be implemented, in which synchronous errors are taken as the measure of similarity of the dynamical patterns generated from different systems.
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Compared with the methods based on signal processing, this approach appears to need less time-domain information for recognition and is more effective for high speed applications. Simulation is included to show the effectiveness of the approach.
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- Deterministic Identification of Dynamical Systems : Christiaan Heij : !
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- DETERMINISTIC LEARNING OF NONLINEAR DYNAMICAL SYSTEMS.
The extended Melnikov method yields the novel result that motions with transitions are chaotic regardless of whether the excitation is deterministic or stochastic. It explains the role in the occurrence of transitions of the characteristics of the system and its deterministic or stochastic excitation, and is a powerful modeling and identification tool. The book is designed primarily for readers interested in applications.
Deterministic Identification of Dynamical Systems by Christiaan Heij - avuquvidymuc.ml
The level of preparation required corresponds to the equivalent of a first-year graduate course in applied mathematics. No previous exposure to dynamical systems theory or the theory of stochastic processes is required. The theoretical prerequisites and developments are presented in the first part of the book.
The second part of the book is devoted to applications, ranging from physics to mechanical engineering, naval architecture, oceanography, nonlinear control, stochastic resonance, and neurophysiology. A specialist in flow-structure interaction, he is the coauthor of Wind Effects on Structures and was the recipient of the Federal Engineer of the Year award. Add to Cart.
More about this book. Chapter 1 [PDF].
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