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Modeling and simulation of systems using MATLAB and Simulink / Devendra K. Chaturvedi.

By: Chaturvedi, Devendra KMaterial type: TextTextLanguage: English Publication details: Boca Raton : CRC Press, c2010. Description: xxiii, 709 p. : ill. ; 27 cm. +ISBN: 9781439806722 (alk. paper); 1439806721Subject(s): Systems engineering -- Data processing | Systems engineering -- Computer programs | Computer simulation | Computer-aided engineeringDDC classification: 620.001171 LOC classification: TA168 | .C474 2010Other classification: ZQ 5380 Online resources: WorldCat details | E-book Fulltext
Contents:
TOC 1 Introduction to Systems -- 2 Systems Modeling -- 3 Formulation of State Space Model of Systems -- 4 Model Order Reduction -- 5 Analogous of Linear Systems -- 6 Interpretive Structural Modeling -- 7 System Dynamics Techniques -- 8 Simulation -- 9 Nonlinear and Chaotic System -- 10 Modeling with Artificial Neural Network -- 11 Modeling Using Fuzzy Systems -- 12 Discrete-Event Modeling and Simulation
Summary: Summary: "Not only do modeling and simulation help provide a better understanding of how real-world systems function, they also enable us to predict system behavior before a system is actually built and analyze systems accurately under varying operating conditions. Modeling and Simulation of Systems Using MATLAB and Simulink provides comprehensive, state-of-the-art coverage of all the important aspects of modeling and simulating both physical and conceptual systems. Various real-life examples show how simulation plays a key role in understanding real-world systems. The author also explains how to effectively use MATLAB and Simulink software to successfully apply the modeling and simulation techniques presented." "After introducing the underlying philosophy of systems, the book offers step-by-step procedures for modeling different types of systems using modeling techniques, such as the graph-theoretic approach, interpretive structural modeling, and system dynamics modeling. It then explores how simulation evolved from pre-computer days into the current science of today. The text also presents modern soft computing techniques, including artificial neural networks, fuzzy systems, and genetic algorithms, for modeling and simulating complex and nonlinear systems. The final chapter addresses discrete systems modeling."--BOOK JACKET.
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Non-fiction 620.001171 CHM 2010 (Browse shelf(Opens below)) Not for loan
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Text Text EWU Library
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Total holds: 0

Includes bibliographical references and index.

TOC 1 Introduction to Systems --
2 Systems Modeling --
3 Formulation of State Space Model of Systems --
4 Model Order Reduction --
5 Analogous of Linear Systems --
6 Interpretive Structural Modeling --
7 System Dynamics Techniques --
8 Simulation --
9 Nonlinear and Chaotic System --
10 Modeling with Artificial Neural Network --
11 Modeling Using Fuzzy Systems --
12 Discrete-Event Modeling and Simulation

Summary:
"Not only do modeling and simulation help provide a better understanding of how real-world systems function, they also enable us to predict system behavior before a system is actually built and analyze systems accurately under varying operating conditions. Modeling and Simulation of Systems Using MATLAB and Simulink provides comprehensive, state-of-the-art coverage of all the important aspects of modeling and simulating both physical and conceptual systems. Various real-life examples show how simulation plays a key role in understanding real-world systems. The author also explains how to effectively use MATLAB and Simulink software to successfully apply the modeling and simulation techniques presented." "After introducing the underlying philosophy of systems, the book offers step-by-step procedures for modeling different types of systems using modeling techniques, such as the graph-theoretic approach, interpretive structural modeling, and system dynamics modeling. It then explores how simulation evolved from pre-computer days into the current science of today. The text also presents modern soft computing techniques, including artificial neural networks, fuzzy systems, and genetic algorithms, for modeling and simulating complex and nonlinear systems. The final chapter addresses discrete systems modeling."--BOOK JACKET.

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