Recent Advances In Modeling Analysis And Systems Control

This book describes recent developments in a wide range of areas, including the modeling, analysis and control of dynamical systems, and explores related applications. The book provided a forum where researchers have shared their ideas, results on theory, and experiments in application problems. The current literature devoted to dynamical systems is quite large, and the authors' choice for the considered topics was motivated by the following considerations. Firstly, the mathematical jargon for systems theory remains quite complex and the authors feel strongly that they have to maintain connections between the people of this research field. Secondly, dynamical systems cover a wider range of applications, including engineering, life sciences and environment. The authors consider that the book is an important contribution to the state of the art in the fuzzy and dynamical systems areas. .

This book describes recent developments in a wide range of areas, including the modeling, analysis and control of dynamical systems, and explores related applications. The book provided a forum where researchers have shared their ideas, results on theory, and experiments in application problems. The current literature devoted to dynamical systems is quite large, and the authors’ choice for the considered topics was motivated by the following considerations. Firstly, the mathematical jargon for systems theory remains quite complex and the authors feel strongly that they have to maintain connections between the people of this research field. Secondly, dynamical systems cover a wider range of applications, including engineering, life sciences and environment. The authors consider that the book is an important contribution to the state of the art in the fuzzy and dynamical systems areas.

​This broad-ranging text/reference presents a fascinating review of the state of the art of modeling and simulation, highlighting both the seminal work of preeminent authorities and exciting developments from promising young researchers in the field. Celebrating the 50th anniversary of the Winter Simulation Conference (WSC), the premier international forum for disseminating recent advances in the field of system simulation, the book showcases the historical importance of this influential conference while also looking forward to a bright future for the simulation community. Topics and features: examines the challenge of constructing valid and efficient models, emphasizing the benefits of the process of simulation modeling; discusses model calibration, input model risk, and approaches to validating emergent behaviors in large-scale complex systems with non-linear interactions; reviews the evolution of simulation languages, and the history of the Time Warp algorithm; offers a focus on the design and analysis of simulation experiments under various goals, and describes how data can be “farmed” to support decision making; provides a comprehensive overview of Bayesian belief models for simulation-based decision making, and introduces a model for ranking and selection in cloud computing; highlights how input model uncertainty impacts simulation optimization, and proposes an approach to quantify and control the impact of input model risk; surveys the applications of simulation in semiconductor manufacturing, in social and behavioral modeling, and in military planning and training; presents data analysis on the publications from the Winter Simulation Conference, offering a big-data perspective on the significant impact of the conference. This informative and inspiring volume will appeal to all academics and professionals interested in computational and mathematical modeling and simulation, as well as to graduate students on the path to form the next generation of WSC pioneers.

The current literature on dynamic systems is quite comprehensive, and system theory’s mathematical jargon can remain quite complicated. Thus, there is a need for a compendium of accessible research that involves the broad range of fields that dynamic systems can cover, including engineering, life sciences, and the environment, and which can connect researchers in these fields. The Handbook of Research on Modeling, Analysis, and Control of Complex Systems is a comprehensive reference book that describes the recent developments in a wide range of areas including the modeling, analysis, and control of dynamic systems, as well as explores related applications. The book acts as a forum for researchers seeking to understand the latest theory findings and software problem experiments. Covering topics that include chaotic maps, predictive modeling, random bit generation, and software bug prediction, this book is ideal for professionals, academicians, researchers, and students in the fields of electrical engineering, computer science, control engineering, robotics, power systems, and biomedical engineering.

Process Modelling and Model Analysis describes the use of models in process engineering. Process engineering is all about manufacturing--of just about anything! To manage processing and manufacturing systematically, the engineer has to bring together many different techniques and analyses of the interaction between various aspects of the process. For example, process engineers would apply models to perform feasibility analyses of novel process designs, assess environmental impact, and detect potential hazards or accidents. To manage complex systems and enable process design, the behavior of systems is reduced to simple mathematical forms. This book provides a systematic approach to the mathematical development of process models and explains how to analyze those models. Additionally, there is a comprehensive bibliography for further reading, a question and answer section, and an accompanying Web site developed by the authors with additional data and exercises. Introduces a structured modeling methodology emphasizing the importance of the modeling goal and including key steps such as model verification, calibration, and validation Focuses on novel and advanced modeling techniques such as discrete, hybrid, hierarchical, and empirical modeling Illustrates the notions, tools, and techniques of process modeling with examples and advances applications

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues, survival analysis, and time series. The volume presents new results with potential for solving real-life problems and provides novel methods for solving these problems by analyzing the relevant data. The use of recent advances in different fields is emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural computing, and bioinformatics. Sample Chapter(s). Chapter 1: An approach to Stochastic Process using Quasi-Arithmetic Means (373 KB). Contents: Stochastic Processes and Models; Distributions; Insurance; Stochastic Modeling for Healthcare Management; Markov and Semi Markov Models; Parametric/Non-Parametric; Dynamical Systems/Forecasting; Modeling and Stochastic Modeling; Statistical Applications in Socioeconimic Problems; Sampling and Optimization Problems; Data Mining and Applications; Clustering and Classification; Applications of Data Analysis; Miscellaneous. Readership: Researchers in probability and statistics, stochastics and fuzzy logic.

The Text Is Written From The Engineer'S Point Of View To Explain The Basic Oncepts Involved In Feedback Control Theory. The Material In The Text Has Been Organized For Gradual And Sequential Development Of Control Theory Starting With A Statement Of The Task Of A Control Engineer At The Very Outset. The Book Is Tended For An Introductory Undergraduate Course In Control Systems For Engineering Students.This Text Presents A Comprehensive Analysis And Design Of Continuous-Time Control Systems And Includes More Than Introductory Material For Discrete Systems With Adequate Guidelines To Extend The Results Derived In Connection Continuous-Time Systems. The Prerequisite For The Reader Is Some Elementary Owledge Of Differential Equations, Vector-Matrix Analysis And Mechanics. Transfer Function And State Variable Models Of Typical Components And Subsystems Have Been Derived In The Appendix At The End Of The Book.Most Of The Materials Including Solved And Unsolved Problems Presented In The Book Have Been Class-Tested In Senior Undergraduates And First Year Graduate El Courses In The Field Of Control Systems At The Electronics And Telecommunication Engineering Department, Jadavpur University. Matlab Is The Most Widely Used Cad Software Package In Universities Throughout The World. Some Representative Matlab Scripts Used For Solving Problems Are Cluded At The End Of Each Chapter. The Detailed Design Steps Of Fuzzy Logic Based Controller Using Simulink And Matlab Has Been Provided In The Book To Give The Student A Head Start In This Emerging Discipline.A Chapter Has Been Included To Deal With Nonlinear Components And Their Analysis G Matlab And Simulink Through User Defined S-Functions. Finally, A Chapter Has Been Included To Deal With The Implementation Of Digital Controllers On Finite Bit Computer, To Bring Out The Problems Associated With Digital Trollers. In View Of Extensive Use Of Matlab For Rapid Verification Of Controller Designs, Some Notes For Using Matlab Script M-Files And Function M-Files Are Included At The End Of The Book.

This book aims to introduce the state-of-the-art research of stability/performance analysis and optimal synthesis methods for fuzzy-model-based systems. A series of problems are solved with new approaches of design, analysis and synthesis of fuzzy systems, including stabilization control and stability analysis, dynamic output feedback control, fault detection filter design, and reduced-order model approximation. Some efficient techniques, such as Lyapunov stability theory, linear matrix inequality, reciprocally convex approach, and cone complementary linearization method, are utilized in the approaches. This book is a comprehensive reference for researchers and practitioners working on intelligent control, model reduction, and fault detection of fuzzy systems, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts and methodologies with theoretical and practical significance in system analysis and control synthesis.