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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 151,92
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Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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EUR 174,80
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Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 166,29
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 75,56
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | As data systems become more effective, more and more mathematical approaches have been applied to real-world applications to achieve exceptional outcomes. Fractional approaches (such as fractional calculus, fractional Fourier analysis, and the linear canonical transform) are gaining importance in the area of mathematics and are gaining attention from the community of applied mathematicians. The theory and method of fractional domain analysis may further define the dynamic process of signal translation from time domains to frequency domains, creating a new avenue for non-stationary signal analyses and treatment studies. In technical domains such as the radar, communications, and sonar domains, fractional approaches are preferable to traditional integral methods because they offer novel concepts, procedures, and ideas. Due to the unpredictability of the sent signal in actual engineering systems and the effect of different disturbances and noises on the transmission process, despite the numerous benefits of these new fractional approaches, a few critical issues still need to be resolved. Simultaneously, fraction theory is confronted with several practical limits in engineering, such as sampling and filtering in the sphere of multidimensional signals. This Special Issue focuses on the current successes and potential difficulties of fractional techniques in engineering theory and applications.
Da: Buchpark, Trebbin, Germania
EUR 84,29
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book aims to bring together the latest innovative knowledge, analysis, and synthesis of fractional control problems of nonlinear systems as well as some related applications. Fractional order systems (FOS) are dynamical systems that can be modelled by a fractional differential equation carried with a non-integer derivative. In the last few decades, the growth of science and engineering systems has considerably stimulated the employment of fractional calculus in many subjects of control theory, for example, in stability, stabilization, controllability, observability, observer design, and fault estimation. The application of control theory in FOS is an important issue in many engineering applications. So, to accurately describe these systems, the fractional order differential equations have been introduced.
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Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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EUR 180,02
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Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2023
ISBN 10: 3031379691 ISBN 13: 9783031379697
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: new. Hardcover. This book presents the separation principle which is also known as the principle of separation of estimation and control and states that, under certain assumptions, the problem of designing an optimal feedback controller for a stochastic system can be solved by designing an optimal observer for the system's state, which feeds into an optimal deterministic controller for the system. Thus, the problem may be divided into two halves, which simplifies its design. In the context of deterministic linear systems, the first instance of this principle is that if a stable observer and stable state feedback are built for a linear time-invariant system (LTI system hereafter), then the combined observer and feedback are stable. The separation principle does not true for nonlinear systems in general. Another instance of the separation principle occurs in the context of linear stochastic systems, namely that an optimum state feedback controller intended to minimize a quadratic cost is optimal forthe stochastic control problem with output measurements. The ideal solution consists of a Kalman filter and a linear-quadratic regulator when both process and observation noise are Gaussian. The term for this is linear-quadratic-Gaussian control. More generally, given acceptable conditions and when the noise is a martingale (with potential leaps), a separation principle, also known as the separation principle in stochastic control, applies when the noise is a martingale (with possible jumps). Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Buchpark, Trebbin, Germania
EUR 92,34
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Seiten: 456 | Sprache: Englisch | Produktart: Bücher | This book presents the separation principle which is also known as the principle of separation of estimation and control and states that, under certain assumptions, the problem of designing an optimal feedback controller for a stochastic system can be solved by designing an optimal observer for the system's state, which feeds into an optimal deterministic controller for the system. Thus, the problem may be divided into two halves, which simplifies its design. In the context of deterministic linear systems, the first instance of this principle is that if a stable observer and stable state feedback are built for a linear time-invariant system (LTI system hereafter), then the combined observer and feedback are stable. The separation principle does not true for nonlinear systems in general. Another instance of the separation principle occurs in the context of linear stochastic systems, namely that an optimum state feedback controller intended to minimize a quadratic cost is optimal forthe stochastic control problem with output measurements. The ideal solution consists of a Kalman filter and a linear-quadratic regulator when both process and observation noise are Gaussian. The term for this is linear-quadratic-Gaussian control. More generally, given acceptable conditions and when the noise is a martingale (with potential leaps), a separation principle, also known as the separation principle in stochastic control, applies when the noise is a martingale (with possible jumps).
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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