JASON L SPEYER (25 Ergebnisse)

As New. UK stocked, available immediately. Hardcover, published by the Society for Industrial and Applied Mathematics in 2010. An unused and sealed copy. Weight (unpacked) is 856 grams.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New. 2010. Hardcover. . . . . .

Zustand: new.

Zustand: New.

Hardback. Zustand: New. The performance of a process, such as aircraft fuel consumption, can be enhanced when its most effective controls and operating points are determined. Primer on Optimal Control Theory provides an introduction to the theory behind analysing these processes and finding the best controls, which provides a sound basis for those wishing to tackle more advanced literature. The book presents the important concepts of weak and strong control variations leading to local necessary conditions, and also global sufficiency of Hamilton-Jacobi-Bellman theory. It also gives the second variation for local optimality where the associated Riccati equation is derived from the transition matrix of the Hamiltonian system. These ideas lead naturally to the development of H2 and H? synthesis algorithms. This book will enable applied mathematicians, engineers, scientists, biomedical researchers, and economists to understand and implement optimal control theory at a level of sufficient generality and applicability for most practical purposes.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Zustand: New. 2011. hardcover. . . . . .

Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. 2010. Hardcover. . . . . . Books ship from the US and Ireland.

Hardback. Zustand: New. Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H-inf controllers and system robustness.Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application.

Hardcover. Zustand: new. Hardcover. Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H-inf controllers and system robustness.Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Hardback. Zustand: New. New copy - Usually dispatched within 4 working days.

Zustand: New. 2011. hardcover. . . . . . Books ship from the US and Ireland.

Zustand: New.

Hardback. Zustand: New. The performance of a process, such as aircraft fuel consumption, can be enhanced when its most effective controls and operating points are determined. Primer on Optimal Control Theory provides an introduction to the theory behind analysing these processes and finding the best controls, which provides a sound basis for those wishing to tackle more advanced literature. The book presents the important concepts of weak and strong control variations leading to local necessary conditions, and also global sufficiency of Hamilton-Jacobi-Bellman theory. It also gives the second variation for local optimality where the associated Riccati equation is derived from the transition matrix of the Hamiltonian system. These ideas lead naturally to the development of H2 and H? synthesis algorithms. This book will enable applied mathematicians, engineers, scientists, biomedical researchers, and economists to understand and implement optimal control theory at a level of sufficient generality and applicability for most practical purposes.

Hardback. Zustand: New. Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H-inf controllers and system robustness.Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application.

Hardcover. Zustand: new. Hardcover. Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H-inf controllers and system robustness.Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.