دانلود کتاب Computational Methods for Modeling of Nonlinear Systems

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Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank mat.  Read more... Front Cover; Random Processes in Nonlinear Control Systems; Copyright Page; Editor's Foreword; Preface; Contents; INTRODUCTION; Chapter 1. NONLINEAR TRANSFORMATIONS WITHOUT FEEDBACK; Chapter 2. NONLINEAR TRANSFORMATION WITH FEEDBACK. STATIONARY STATES; Chapter 3. NONLINEAR TRANSFORMATIONS WITH FEEDBACK. NONSTATIO NARY STATES; Chapter 4. EXTREMAL SYSTEMS; Appendix I: FUNCTIONS my(mx, sx), h1(mx, sx), a2 (mx, sx), AND a3 (mx, sx) FOR SEVERAL TYPICAL NONLINEARITIES. Appendix II: REPRESENTATION OF A LAGLESS NONLINEAR TRANSFORMATION IN THE FORM OF AN INTEGRAL TRANSFORMATION IN A COMPLEX REGION. THE THEOREM OF R. PRICEAppendix III: COMPUTATION OF THE INTEGRALS In; Appendix IV: THE COEFFICIENTS OF STATISTICAL LINEARIZATION h1(a, s) AND h2(a, s) FOR TYPICAL NONLlNEARITIES; Appendix V: ELEMENTARY STATEMENTS ON THE THEORY OF MARKOV PROCESSES; Related Literature; Bibliography; Author Index; Subject Index