Timothy Bower

Introduction to Computational Engineering with MATLAB® (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book teaches readers how to use MATLAB programming to solve numerical engineering problems. The book focuses on computational engineering with the objective of helping engineering students improve their numerical problem-solving skills.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 438
• Erscheinungstermin: 28. September 2022
• Englisch
• ISBN-13: 9781000713138
• Artikelnr.: 65002885

Autorenporträt

Tim Bower is an Associate Professor of Robotics and Automation Engineering Technology and Computer Systems Technology at Kansas State University Salina. He received the B.S. Electrical Engineering degree from Kansas State University (K-State) in 1987 and the M.S. Electrical Engineering degree from the University of Kansas in 1990. He was a Senior Member of the Technical Staff at Sprint's Local Telephone Division from 1989 to 1998. From 1998 to 2003, he was a systems administration manager and instructor at Kansas State University in Manhattan Kansas while taking graduate course work in Computer Science. He joined the faculty of K-State's campus in Salina Kansas in 2004. He teaches undergraduate courses related to programming in C, Python, and MATLAB, robotics programming, machine vision, numerical computation, operating systems, data structures and algorithms, and systems administration.

Away from teaching, he enjoys spending time with his wife, three grown children, and five grandchildren.

Inhaltsangabe

1. MATLAB Programming. 1.1. The MATLAB Development Environment. 1.2.
Variables and Values. 1.3. MATLAB Scripts. 1.4. Input and Output. 1.5. For
Loops. 1.6. Control Constructs. 1.7. Vectors and Matrices in MATLAB. 1.8.
MATLAB Functions. 1.9. Functions Operating on Vectors. 1.10. Importing Data
Into MATLAB. 1.11. Text Strings in MATLAB. 1.12. Exercises. 2. Graphical
Data Analysis. 2.1. Using the Plot Tool. 2.2. Basic Line Plots. 2.3. 3-D
Plots. 2.4. Exercises. 3. Statistical Data Analysis. 3.1. Introduction to
Statistics. 3.2. Common Statistical Functions. 3.3. Moving Window
Statistics. 3.4. Probability Distributions. 3.5. Generating Random Numbers.
3.6. Statistics on Matrices. 3.7. Plots of Statistical Data. 3.8. Central
Limit Theorem. 3.9. Sampling and Confidence Intervals. 3.10. Statistical
Significance. 3.11. Exercises. 4. Using the Symbolic Math Toolbox. 4.1.
Throwing a Ball Up. 4.2. Symbolic Algebra. 4.3. Symbolic Calculus. 4.4.
Symbolic Differential Equations. 4.5. Exercises. 5. Introduction to Linear
Algebra. 5.1. Working with Vectors. 5.2. Working with Matrices. 5.3.
Geometric Transforms. 5.4. Systems of Linear Equations. 5.5. Elimination.
5.6. LU Decomposition. 5.7. Linear System Applications. 5.8.
Under-determined Systems. 5.9. Over-determined Systems and Vector
Projections. 5.10. Least Squares Regression. 5.11. Left-Divide Operator.
5.12. Exercises. 6. Application of Eigenvalues and Eigenvectors. 6.1.
Introduction to Eigenvalues and Eigenvectors. 6.2. Eigenvector Animation.
6.3. Finding Eigenvalues and Eigenvectors. 6.4. Properties of Eigenvalues
and Eigenvectors. 6.5. Diagonalization and Powers of A. 6.6. Change of
Basis and Difference Equations. 6.7. Systems of Linear ODEs. 6.8. Singular
Value Decomposition (SVD). 6.9. Principal Component Analysis (PCA). 6.10.
Eigenvector Animation Code. 6.11. Exercises. 7. Computational Numerical
Methods. 7.1. Optimization. 7.2. Data Interpolation. 7.3. Numerical
Differentiation. 7.4. Numerical Integration. 7.5. Numerical Differential
Equations. 7.6. Exercises. A. Linear Algebra Appendix. B. The Number e.
Bibliography. Index.