Published 1973 in Toronto .
Written in EnglishRead online
|Contributions||Toronto, Ont. University.|
|The Physical Object|
|Pagination||v, 286 leaves.|
|Number of Pages||286|
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Buy Nonlinear Stochastic Systems in Physics and Mechanics on FREE SHIPPING on qualified orders Nonlinear Stochastic Systems in Physics and Mechanics: Bellomo, Nicola, Riganti, Ricardo: : BooksCited by: For systems with lumped parameters and without memory an adequate model are ordinary differential equations (ODE), where the vector field f describes the directional field of the dynamics, depending on the systems parameters p ∈ R m.
(Time dependent fields f(x,t) Cited by: 7. This book is a complete treatise on the theory of nonlinear dynamics of chaotic and stochastic systems. It contains both an exhaustive introduction to the subject as well as a detailed discussion of fundamental problems and research results in a field to which the authors have Cited by: Abstract.
The paper discusses the transition from deterministic to stochastic dynamic systems under external or internal excitations. For this purpose, we apply harmonic excitation models with frequency fluctuations by white noise and derive invariant measures as stationary solutions of associated Fokker-Planck by: Atalik T, Utku S () Stochastic linearization of multi-degree of freedom nonlinear systems.
Earthquake Eng Struct Dyn 4(4) – Booton R () The analysis of nonlinear control systems. Nonlinear systems disturbed by Gaussian white noises (or by signals obtained from Gaussian white noises) can sometimes be analysed by setting up and solving the Fokker–Planck equation for the probability density in state space.
In the present paper a simplified derivation of the Fokker–Planck equation is given. The uniqueness of the steady-state solution is by: ESTIMATION AND ANALYSIS OF NONLINEAR STOCHASTIC SYSTEMS by Steven Irl Marcus Submitted to the Department of Electrical Engineering on in partial fulfillment of the requirements for the Degree of Doctor of Philosophy ABSTRACT The algebraic and geometric structure of certain classes of nonlinear stochastic systems is exploited in order to.
On the Analysis of Nonlinear Stochastic Systems Example 5 The nonlinear system given in Example 3 is solved by the Volterra-Wiener Functional method as follows. Given the nonlinear system: x" + 3x' + 2x + g(x')2 = r(t) (28) where Analysis of nonlinear stochastic systems book - oo} denotes a sample function from a strictly stationary source in which the moments of all orders are bounded, and g is a by: 1.
simple, reasonably general, nonlinear system theory could be developed. Hand in hand with this viewpoint was the feeling that many of the approaches useful for linear systems ought to be extensible to the nonlinear theory.
This is a key point if the theory is. This paper deals with the analysis of the dynamic behavior of nonlinear systems subject to probabilistic uncertainties in the physical parameters. The Polynomial Chaos Expansion PCE is proposed to resolve this problem.
The main objective is to. Zakai equation of nonlinear filtering is an important source for studying linear and nonlinear stochastic partial differential equations. In the Hilbert space L 2 (0, T ; H), a set Z depends on three constants K, L, M and two sequences μ n, ν n of numbers such that μ n, ν n ν n 0 and μ n, ν n ν n 0 as n PP.
Analysis of Nonlinear Stochastic Systems with Jumps Generated by Erlang Flow of Events. Alexander S. Kozhevnikov, Konstantin A. Rybakov ism to the probabilistic analysis problem for the stochastic systems with jumps.
This method allows to get a solution of the analysis problem in an explicit form. Here, the authors present modern methods of analysis for Analysis of nonlinear stochastic systems book systems which may occur in fields such as physics, chemistry, biology, or economics. They concentrate on the following topics, specific for such systems: (a) constructive existence results and regularity theorems for all weak solutions; (b).
Linear Stochastic Systems At the degree of generality of (), there is not much more one can say about the properties of the process xk based on those of x 0 and w. For the rest of this chapter, we shall concentrate on second order analysis of linear stochastic systems.
We shall see that quite a lot of concrete results can be obtained in File Size: KB. The purpose of Part 1 of this paper is to provide a review of recent results from through in the area of theoretical aspects of statistical and equivalent linearization in the analysis of structural and mechanical nonlinear stochastic dynamic by: Nonlinear dynamics as manifested by deterministic and stochastic evolution models of complex behavior has entered statistical physics, physical chemistry, biophysics, geophysics, astrophysics, theoretical ecology, semiconductor physics and -optics, etc.
Stochastic Sparse-grid Collocation Algorithm (SSCA) for Periodic Steady-State Analysis of Nonlinear System with Process Variations Jun Tao1,XuanZeng∗1,WeiCai2, Yangfeng Su3, Dian Zhou1,4, Charles Chiang5 1ASIC & System State Key Lab., Microelectronics Dept., Fudan University, ShanghaiP.R.
China 2Department of Mathematics, University of North Carolina at Charlotte, Charlotte, NC. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic.
The most direct link between chaos theory and the real world is the analysis of time series from real systems 5/5(1). CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The algebraic and geometric structure of certain classes of nonlinear stochastic systems is exploited in order to obtain useful stability and estimation results.
First, the class of bilinear stochastic systems (or linear systems with multiplicative noise) is discussed. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 62, NO. 9, SEPTEMBER Stability Analysis of Impulsive Stochastic Nonlinear Systems Wei Ren and Junlin Xiong, Member, IEEE Abstract—This paper studies stochastic input-to-state stability and stochastic global stability for impulsive stochastic nonlinearFile Size: KB.
Section is devoted to the methods based on the canonical expansions and the integral canonical representations. Sections and contain the applications of the nonlinear stochastic systems theory methods to the analysis, modeling and conditionally optimal filtering.
() A partial history of the early development of continuous-time nonlinear stochastic systems theory. Automatica() First and Second Order Necessary Conditions for Stochastic Optimal Control by: A disease transmission model of SEIR type is discussed in a stochastic point of view.
We start by formulating the SEIR epidemic model in form of a system of nonlinear differential equations and then change it to a system of nonlinear stochastic differential equations (SDEs). The numerical simulation of the resulting SDEs is done by Euler-Maruyama scheme and the parameters are estimated by Author: D.
Ndanguza, I. Mbalawata, J. Nsabimana. Modeling and Analysis of Stochastic Hybrid Systems. For nonlinear systems near chemical instabilities, where fluctuations and correlations may invalidate the deterministic equations, the.
Seminar talk entitled “Subspace method for the probabilistic solutions of large-scale nonlinear stochastic dynamic systems” in the Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, August 9,Shanghai, China.
The book deals with the analysis of nonlinear systems with stochastic inputs and establishes the performance metrics of communication systems with regard to nonlinearity.
The relationship between nonlinear system parameters (which are model dependent) and system performance figures of merit is established when the input to. In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of the input.
Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many other scientists because most systems are. The most important numerical tools needed in the analysis of chaotic systems performing chaos synchronization and chaotic communications are discussed in this chapter.
Basic concepts, theoretical framework, and computer algorithms are reviewed. The subjects covered include the concepts and numerical simulations of stochastic nonlinear systems, the complexity of a chaotic attractor.
() Highly nonlinear neutral stochastic differential equations with time-dependent delay and the Euler–Maruyama method. Mathematical and Computer Modelling() Numerical Approximation of Stochastic Systems for Composite Materials Based on Markov by: Stochastic Numerical Methods introduces at Master level the numerical methods that use probability or stochastic concepts to analyze random processes.
The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have.
Stochastic analysis of nonlinear, nonstationary water storage systems in continuous and discrete time. Austin, Tex.: Center for Research in Water Resources, Bureau of Engineering Research, University of Texas at Austin, .
Professor J. Sun started working on random vibrations of nonlinear systems when he did his doctoral research at the University of California-Berkeley in s. Since joining the faculty at the University of Delaware inhe has successfully completed several research projects on analysis and control of stochastic systems.
Building on the author’s more than 35 years of teaching experience, Modeling and Analysis of Stochastic Systems, Third Edition, covers the most important classes of stochastic processes used in the modeling of diverse each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage Cited by: Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for stochastic systems with traditional and emerging engineering-oriented complexities.
The book begins with an overview of the relevant background, motivation, and research problems, and then:Brand: CRC Press. As one of the most important control models, stochastic systems widely exist in real world, such as mobile sensor networks, multi-agent systems, unmanned aerial vehicles and aircrafts, etc.
However, due to the influence of random factors, there are still many challenging issues arising from nonlinear stochastic systems. ISBN: X OCLC Number: Description: xx, pages ; 25 cm.
Contents: I: A Summary of the Decomposition Method.- 1: The Decomposition Method.- Introduction.- Summary of the Decomposition Method.- Generation of the An Polynomials.- The An for Differential Nonlinear Operators.- Convenient Computational Forms for the An Polynomials.- The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter Chapter 1 and Chapter 2 treat design techniques based on linearization of nonlinear systems.
An analysis of nonlinear system over quantum mechanics is discussed in Chapter 3. Chapter 4 to Chapter 7 are estimation methods using Kalman filtering while solving nonlinear control systems using iterative Cited by: 1.
the control of nonlinear stochastic systems and thus there is concern about the ability of the controller to adequately regulate the system. An alternative approach to cope with such systems is to avoid the need to build the traditional “open-loop” model for the system.
Through the avoidance of model. non-linear filtering problem is not solved in this book and only approximate non-linear filtering algorithms are de- veloped. The present book is a combination of stochastic signal analysis and continuous-time filtering theory.
There are, of course, numerous engineering texts on stochastic signals and systems (some recent publications include. ( views) An Introduction to Nonlinearity in Control Systems by Derek Atherton - BookBoon, The book is concerned with the effects of nonlinearity in feedback control systems and techniques which can be used to design feedback loops containing nonlinear elements.
The material is of an introductory nature but hopefully gives an overview. By applying fuzzy-logic systems to approximate the unknown nonlinearities, a novel adaptive finite-time control strategy is proposed. However, due to the existence of approximation errors, the existing finite-time stability criterion cannot be used to analyze the stability of stochastic nonlinear by: 5.The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing.
Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the : Springer London.the system and inadequate to deal with nonlinear and/or dynamic problems.
Solutions based on the variability response function are accurate only for small values of variability of the stochastic properties of the system and inadequate to deal with nonlinear and/or dynamic problems.