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Stochastic versus Deterministic Systems of Differential Equations

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Release : 2003-12-05
Genre : Mathematics
Kind : eBook
Book Rating : 027/5 ( reviews)

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Book Synopsis Stochastic versus Deterministic Systems of Differential Equations by : G. S. Ladde

Download or read book Stochastic versus Deterministic Systems of Differential Equations written by G. S. Ladde. This book was released on 2003-12-05. Available in PDF, EPUB and Kindle. Book excerpt: This peerless reference/text unfurls a unified and systematic study of the two types of mathematical models of dynamic processes-stochastic and deterministic-as placed in the context of systems of stochastic differential equations. Using the tools of variational comparison, generalized variation of constants, and probability distribution as its met

Stochastic versus Deterministic Systems of Differential Equations

Download Stochastic versus Deterministic Systems of Differential Equations PDF Online Free

Author :
Release : 2003-12-05
Genre : Mathematics
Kind : eBook
Book Rating : 028/5 ( reviews)

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Book Synopsis Stochastic versus Deterministic Systems of Differential Equations by : G. S. Ladde

Download or read book Stochastic versus Deterministic Systems of Differential Equations written by G. S. Ladde. This book was released on 2003-12-05. Available in PDF, EPUB and Kindle. Book excerpt: This peerless reference/text unfurls a unified and systematic study of the two types of mathematical models of dynamic processes-stochastic and deterministic-as placed in the context of systems of stochastic differential equations. Using the tools of variational comparison, generalized variation of constants, and probability distribution as its met

Stochastic Versus Deterministic Systems Of Iterative Processes

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Author :
Release : 2024-04-22
Genre : Mathematics
Kind : eBook
Book Rating : 49X/5 ( reviews)

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Book Synopsis Stochastic Versus Deterministic Systems Of Iterative Processes by : Gangaram S Ladde

Download or read book Stochastic Versus Deterministic Systems Of Iterative Processes written by Gangaram S Ladde. This book was released on 2024-04-22. Available in PDF, EPUB and Kindle. Book excerpt: Continuous state dynamic models can be reformulated into discrete state processes. This process generates numerical schemes that lead theoretical iterative schemes. This type of method of stochastic modelling generates three basic problems. First, the fundamental properties of solution, namely, existence, uniqueness, measurability, continuous dependence on system parameters depend on mode of convergence. Second, the basic probabilistic and statistical properties, namely, the behavior of mean, variance, moments of solutions are described as qualitative/quantitative properties of solution process. We observe that the nature of probability distribution or density functions possess the qualitative/quantitative properties of iterative prosses as a special case. Finally, deterministic versus stochastic modelling of dynamic processes is to what extent the stochastic mathematical model differs from the corresponding deterministic model in the absence of random disturbances or fluctuations and uncertainties.Most literature in this subject was developed in the 1950s, and focused on the theory of systems of continuous and discrete-time deterministic; however, continuous-time and its approximation schemes of stochastic differential equations faced the solutions outlined above and made slow progress in developing problems. This monograph addresses these problems by presenting an account of stochastic versus deterministic issues in discrete state dynamic systems in a systematic and unified way.

Linear Systems Control

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Release : 2008-10-13
Genre : Technology & Engineering
Kind : eBook
Book Rating : 861/5 ( reviews)

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Book Synopsis Linear Systems Control by : Elbert Hendricks

Download or read book Linear Systems Control written by Elbert Hendricks. This book was released on 2008-10-13. Available in PDF, EPUB and Kindle. Book excerpt: Modern control theory and in particular state space or state variable methods can be adapted to the description of many different systems because it depends strongly on physical modeling and physical intuition. The laws of physics are in the form of differential equations and for this reason, this book concentrates on system descriptions in this form. This means coupled systems of linear or nonlinear differential equations. The physical approach is emphasized in this book because it is most natural for complex systems. It also makes what would ordinarily be a difficult mathematical subject into one which can straightforwardly be understood intuitively and which deals with concepts which engineering and science students are already familiar. In this way it is easy to immediately apply the theory to the understanding and control of ordinary systems. Application engineers, working in industry, will also find this book interesting and useful for this reason. In line with the approach set forth above, the book first deals with the modeling of systems in state space form. Both transfer function and differential equation modeling methods are treated with many examples. Linearization is treated and explained first for very simple nonlinear systems and then more complex systems. Because computer control is so fundamental to modern applications, discrete time modeling of systems as difference equations is introduced immediately after the more intuitive differential equation models. The conversion of differential equation models to difference equations is also discussed at length, including transfer function formulations. A vital problem in modern control is how to treat noise in control systems. Nevertheless this question is rarely treated in many control system textbooks because it is considered to be too mathematical and too difficult in a second course on controls. In this textbook a simple physical approach is made to the description of noise and stochastic disturbances which is easy to understand and apply to common systems. This requires only a few fundamental statistical concepts which are given in a simple introduction which lead naturally to the fundamental noise propagation equation for dynamic systems, the Lyapunov equation. This equation is given and exemplified both in its continuous and discrete time versions. With the Lyapunov equation available to describe state noise propagation, it is a very small step to add the effect of measurements and measurement noise. This gives immediately the Riccati equation for optimal state estimators or Kalman filters. These important observers are derived and illustrated using simulations in terms which make them easy to understand and easy to apply to real systems. The use of LQR regulators with Kalman filters give LQG (Linear Quadratic Gaussian) regulators which are introduced at the end of the book. Another important subject which is introduced is the use of Kalman filters as parameter estimations for unknown parameters. The textbook is divided into 7 chapters, 5 appendices, a table of contents, a table of examples, extensive index and extensive list of references. Each chapter is provided with a summary of the main points covered and a set of problems relevant to the material in that chapter. Moreover each of the more advanced chapters (3 - 7) are provided with notes describing the history of the mathematical and technical problems which lead to the control theory presented in that chapter. Continuous time methods are the main focus in the book because these provide the most direct connection to physics. This physical foundation allows a logical presentation and gives a good intuitive feel for control system construction. Nevertheless strong attention is also given to discrete time systems. Very few proofs are included in the book but most of the important results are derived. This method of presentation makes the text very readable and gives a good foundation for reading more rigorous texts. A complete set of solutions is available for all of the problems in the text. In addition a set of longer exercises is available for use as Matlab/Simulink ‘laboratory exercises’ in connection with lectures. There is material of this kind for 12 such exercises and each exercise requires about 3 hours for its solution. Full written solutions of all these exercises are available.

Stochastic Versus Deterministic Systems of Iterative Processes

Download Stochastic Versus Deterministic Systems of Iterative Processes PDF Online Free

Author :
Release : 2024-06-06
Genre : Mathematics
Kind : eBook
Book Rating : 473/5 ( reviews)

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Book Synopsis Stochastic Versus Deterministic Systems of Iterative Processes by : Gangaram S Ladde

Download or read book Stochastic Versus Deterministic Systems of Iterative Processes written by Gangaram S Ladde. This book was released on 2024-06-06. Available in PDF, EPUB and Kindle. Book excerpt: State continuous dynamic models can be reformulated into discrete state processes. This process generates numerical schemes that lead theoretical iterative schemes. This type of method of stochastic modelling generates three basic problems. First, the fundamental properties of solution, namely, existence, uniqueness, measurability, continuous dependence on system parameters depend mode of convergence. Second, the basic probabilistic and statistical properties mean, variance, moments of qualitative/quantitative behaviour of solutions are directly described as concept of solution process or via probability distribution or density functions either. Finally, deterministic versus stochastic modelling of dynamic processes is to what extent the stochastic mathematical model differs from the corresponding deterministic model in the absence of random disturbances or fluctuations and uncertainties.Most literature in this subject was developed in the 1950s, and focussed on the theory of systems of continuous and discrete-time deterministic; however, continuous-time and its approximation schemes of stochastic differential equations faced the problems outlined above and made slow progress in developing problems as a result. This monograph addresses these problems by presenting an account of stochastic versus deterministic issues in discrete state dynamic systems in a systematic and unified way.

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