Articles by "MATLAB"

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James R. Chelikowsky ... 224 pages - Publisher: Wiley-VCH; (August, 2018) ... Language: English - AmazonSIN: B07GWWQP2H.

Presents a unique approach to grasping the concepts of quantum theory with a focus on atoms, clusters, and crystals. Quantum theory of atoms and molecules is vitally important in molecular physics, materials science, nanoscience, solid state physics and many related fields. Introductory Quantum Mechanics with MATLAB is designed to be an accessible guide to quantum theory and its applications. The textbook uses the popular MATLAB programming language for the analytical and numerical solution of quantum mechanical problems, with a particular focus on clusters and assemblies of atoms. The textbook is written by a noted researcher and expert on the topic who introduces density functional theory, variational calculus and other practice-proven methods for the solution of quantum-mechanical problems. This important guide: - Presents the material in a didactical manner to help students grasp the concepts and applications of quantum theory. - Covers a wealth of cutting-edge topics such as clusters, nanocrystals, transitions and organic molecules. - Offers MATLAB codes to solve real-life quantum mechanical problems. Written for master's and PhD students in physics, chemistry, material science, and engineering sciences, Introductory Quantum Mechanics with MATLAB contains an accessible approach to understanding the concepts of quantum theory applied to atoms, clusters, and crystals.

William Bober ... 274 pages - Publisher: CRC Press; 1st edition (August, 2017) ... Language: English - ISBN-10: 1138032379 - ISBN-13: 978-1138032378

All disciplines of science and engineering use numerical methods for complex problem analysis, due to the highly mathematical nature of the field. Analytical methods alone are unable to solve many complex problems engineering students and professionals confront. Introduction to MATLAB® Programming for Engineers and Scientists examines the basic elements of code writing, and describes MATLAB® methods for solving common engineering problems and applications across the range of engineering disciplines. The text uses a class-tested learning approach and accessible two-color page design to guide students from basic programming to the skills needed for future coursework and engineering practice. Features: Presents clear and detailed explanations of the basic steps in learning MATLAB * Includes real-world examples from all major engineering disciplines * Prepares students for Numerical numerical Methods methods and other courses that involve MATLAB * Incudes two-color graphics to highlight MATLAB screen shots and step-by-step procedures * Supported by a range of instructor supplements and optional MATLAB code

Ramin S. Esfandiari ... 550 pages - Publisher: CRC Press; 1st edition (June, 2013) ... Language: English - ISBN-10: 1466585692 - ISBN-13: 978-1466585690

Designed to benefit scientific and engineering applications, Numerical Methods for Engineers and Scientists Using MATLAB® focuses on the fundamentals of numerical methods while making use of MATLAB software. The book introduces MATLAB early on and incorporates it throughout the chapters to perform symbolic, graphical, and numerical tasks. The text covers a variety of methods from curve fitting to solving ordinary and partial differential equations. Provides fully worked-out examples showing all details + Confirms results through the execution of the user-defined function or the script file  + Executes built-in functions for re-confirmation, when available + Generates plots regularly to shed light on the soundness and significance of the numerical results. Created to be user-friendly and easily understandable, Numerical Methods for Engineers and Scientists Using MATLAB® provides background material and a broad introduction to the essentials of MATLAB, specifically its use with numerical methods. Building on this foundation, it introduces techniques for solving equations and focuses on curve fitting and interpolation techniques. It addresses numerical differentiation and integration methods, presents numerical methods for solving initial-value and boundary-value problems, and discusses the matrix eigenvalue problem, which entails numerical methods to approximate a few or all eigenvalues of a matrix. The book then deals with the numerical solution of partial differential equations, specifically those that frequently arise in engineering and science. The book presents a user-defined function or a MATLAB script file for each method, followed by at least one fully worked-out example. When available, MATLAB built-in functions are executed for confirmation of the results. A large set of exercises of varying levels of difficulty appears at the end of each chapter. The concise approach with strong, up-to-date MATLAB integration provided by this book affords readers a thorough knowledge of the fundamentals of numerical methods utilized in various disciplines.

Abdelwahab Kharab, Ronald B. Guenther ... 631 pages - Publisher: CRC Press; 4th edition (September, 2018) ... Language: English - ASIN: B07G423TJM by Amazon

Previous editions of this popular textbook offered an accessible and practical introduction to numerical analysis. An Introduction to Numerical Methods: A MATLAB® Approach, Fourth Edition continues to present a wide range of useful and important algorithms for scientific and engineering applications. The authors use MATLAB to illustrate each numerical method, providing full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes a new chapter on Dynamical Systems and Chaos

Darald J. Hartfiel ... 384 pages - Publisher: CRC Press; (November, 2000) ... Language: English - ISBN-10: 1584881089 - ISBN-13: 978-1584881087

Designed for use in a second course on linear algebra, Matrix Theory and Applications with MATLAB covers the basics of the subject-from a review of matrix algebra through vector spaces to matrix calculus and unitary similarity-in a presentation that stresses insight, understanding, and applications. Among its most outstanding features is the integration of MATLAB throughout the text. Each chapter includes a MATLAB subsection that discusses the various commands used to do the computations in that section and offers code for the graphics and some algorithms used in the text. All of the material is presented from a matrix point of view with enough rigor for students to learn to compose arguments and proofs and adjust the material to cover other problems. The treatment includes optional subsections covering applications, and the final chapters move beyond basic matrix theory to discuss more advanced topics, such as decompositions, positive definite matrices, graphics, and topology. Filled with illustrations, examples, and exercises that reinforce understanding, Matrix Theory and Applications with MATLAB allows readers to experiment and visualize results in a way that no other text does. Its rigor, use of MATLAB, and focus on applications better prepares them to use the material in their future work and research, to extend the material, and perhaps obtain new results of their own.

Jasbir S. Arora ... 896 pages - Publisher: Academic Press; 3rd edition (August, 2011) ... Language: English - ISBN-10: 0123813751 - ISBN-13: 978-0123813756

Introduction to Optimum Design 3e describes an organized approach to engineering design optimization in a rigorous yet simplified manner. It illustrates various concepts and procedures with simple examples and demonstrates their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB® are featured as learning and teaching aids. Basic concepts of optimality conditions and numerical methods are described with simple and practical examples, making the material highly teachable and learnable. + Includes applications of optimization methods for structural, mechanical, aerospace, and industrial engineering problems. + Introduction to MATLAB Optimization Toolbox. + Practical design examples introduce students to the use of optimization methods early in the book. + New example problems throughout the text are enhanced with detailed illustrations. + Optimum design with Excel Solver has been expanded into a full chapter. + New chapter on several advanced optimum design topics serves the needs of instructors who teach more advanced courses.

Kelly Bennett ... 664 pages - Publisher: InTech; (September, 2014) ... Language: English - ISBN: 9789535117193 ...

MATLAB is an indispensable asset for scientists, researchers, and engineers. The richness of the MATLAB computational environment combined with an integrated development environment (IDE) and straightforward interface, toolkits, and simulation and modeling capabilities, creates a research and development tool that has no equal. From quick code prototyping to full blown deployable applications, MATLAB stands as a defacto development language and environment serving the technical needs of a wide range of users. As a collection of diverse applications, each book chapter presents a novel application and use of MATLAB for a specific result.

Amir Beck ... 294 pages - Publisher: SIAM-Society for Industrial and Applied Mathematics; (October, 2014) ... Language: English - ISBN-10: 1611973643 - ISBN-13: 978-1611973648 ...

This book provides the foundations of the theory of nonlinear optimization as well as some related algorithms and presents a variety of applications from diverse areas of applied sciences. The author combines three pillars of optimization-theoretical and algorithmic foundation, familiarity with various applications, and the ability to apply the theory and algorithms on actual problems-and rigorously and gradually builds the connection between theory, algorithms, applications, and implementation. Readers will find more than 170 theoretical, algorithmic, and numerical exercises that deepen and enhance the reader's understanding of the topics. The author includes several subjects not typically found in optimization books-for example, optimality conditions in sparsity-constrained optimization, hidden convexity, and total least squares. The book also offers a large number of applications discussed theoretically and algorithmically, such as circle fitting, Chebyshev center, the Fermat-Weber problem, denoising, clustering, total least squares, and orthogonal regression and theoretical and algorithmic topics demonstrated by the MATLAB toolbox CVX and a package of m-files that is posted on the book's web site.

Brian Hahn, Daniel Valentine ... 424 pages - Publisher: Academic Press; 5th edition (January, 2013) ... Language: English - ISBN-10: 0123943981 - ISBN-13: 978-0123943989 ...

The fifth edition of Essential MATLAB for Engineers and Scientists provides a concise, balanced overview of MATLAB's functionality that facilitates independent learning, with coverage of both the fundamentals and applications. The essentials of MATLAB are illustrated throughout, featuring complete coverage of the software's windows and menus. Program design and algorithm development are presented clearly and intuitively, along with many examples from a wide range of familiar scientific and engineering areas. This is an ideal book for a first course on MATLAB or for an engineering problem-solving course using MATLAB, as well as a self-learning tutorial for professionals and students expected to learn and apply MATLAB. Updated with the features of MATLAB R2012b + Expanded discussion of writing functions and scripts + Revised and expanded Part II: Applications + Expanded section on GUIs + More exercises and examples throughout + Companion website for students providing M-files used within the book and selected solutions to end-of-chapter problems.

Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer.

 This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

Dan B. Marghitu, Mihai Dupac, Nels H. Madsen ... 286 pages - Publisher: Springer; (2013)... Language: English - ISBN-10: 1447151097 - ISBN-13: 978-1447151098 ...

Engineering mechanics involves the development of mathematical models of the physical world. Statics addresses the forces acting on and in mechanical objects and systems. Statics with MATLAB® develops an understanding of the mechanical behavior of complex engineering structures and components using MATLAB® to execute numerical calculations and to facilitate analytical calculations. MATLAB® is presented and introduced as a highly convenient tool to solve problems for theory and applications in statics. Included are example problems to demonstrate the MATLAB® syntax and to also introduce specific functions dealing with statics. These explanations are reinforced through figures generated with MATLAB® and the extra material available online which includes the special functions described. This detailed introduction and application of MATLAB® to the field of statics makes Statics with MATLAB® a useful tool for instruction as well as self study, highlighting the use of symbolic MATLAB® for both theory and applications to find analytical and numerical solutions. 

Aaron R. Bradley ... 238 pages -Publisher: Springer; (October, 2011) ... Language: English - ISBN-10: 3642233023 - ISBN-13: 978-3642233029

To learn to program is to be initiated into an entirely new way of thinking about engineering, mathematics, and the world in general. Computation is integral to all modern engineering disciplines, so the better you are at programming, the better you will be in your chosen field. The author departs radically from the typical presentation by teaching concepts and techniques in a rigorous manner rather than listing how to use libraries and functions. He presents pointers in the very first chapter as part of the development of a computational model that facilitates an ab initio presentation of subjects such as function calls, call-by-reference, arrays, the stack, and the heap. The model also allows students to practice the essential skill of memory manipulation throughout the entire course rather than just at the end. As a result, this textbook goes further than is typical for a one-semester course -- abstract data types and linked lists, for example, are covered in depth. The computational model will also serve students in their adventures with programming beyond the course: instead of falling back on rules, they can think through the model to decide how a new programming concept fits with what they already know. The book is appropriate for undergraduate students of engineering and computer science, and graduate students of other disciplines. It contains many exercises integrated into the main text, and the author has made the source code available online.

Stephen J. Chapman ... 592 pages - Publisher: CL Engineering; 4th edition (November, 2007) ... Language: English - ISBN-10: 049524449X - ISBN-13: 978-0495244493 ...

Emphasizing problem-solving skills throughout this very successful book, Stephen Chapman introduces the MATLAB language and shows how to use it to solve typical technical problems. The book teaches MATLAB as a technical programming language showing students how to write clean, efficient, and well-documented programs. It makes no pretense at being a complete description of all of MATLAB's hundreds of functions. Instead, it teaches students how to locate any desired function with MATLAB's extensive on line help facilities. Overall, students develop problem-solving skills and are equipped for future courses and careers using the power of MATLAB.

David E. Hiebeler ... 233 pages - Publisher: Chapman and Hall/CRC; (June, 2015) ... Language: English - ISBN-10: 1466568380 - ISBN-13: 978-1466568389 ... 

The First Book to Explain How a User of R or MATLAB Can Benefit from the Other: In today’s increasingly interdisciplinary world, R and MATLAB® users from different backgrounds must often work together and share code. R and MATLAB® is designed for users who already know R or MATLAB and now need to learn the other platform. The book makes the transition from one platform to the other as quick and painless as possible. Enables R and MATLAB Users to Easily Collaborate and Share Code: The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

Stephen J. Chapman ... 432 pages - Publisher: CL Engineering; 2nd edition (November, 2008) ... Language: English - ISBN-10: 049529568X - ISBN-13: 978-0495295686 ...

Stephen Chapman's Essentials of MATLAB Programming is a successful freshman-level text that is useable in a wide range of courses. This brief text serves two purposes - it teaches how to program using MATLAB as a technical programming language as well as teaching students the basics of computer programming. Using top-down design methodology, the text encourages students to think about the proper design of a program before coding. Problem solving skills as well as the ability to locate desired functions within MATLAB are also presented making this text a useful reference tool.

Machine Learning Classification Algorithms using MatLab [Size: 580 MB] ... This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.

Table of Contents: - Segment 1: Instructor and Course Introduction - Segment 2: MATLAB Crash Course - Segment 3: Grabbing and Importing Dataset - Segment 4: K-Nearest Neighbor - Segment 5: Naive Bayes - Segment 6: Decision Trees - Segment 7: Discriminant Analysis - Segment 8: Support Vector Machines - Segment 9: Error Correcting Output Codes - Segment 10: Classification with Ensembles - Segment 11: Validation Methods - Segment 12: Evaluating Performance. This course is really good for a beginner. It will help you to start from the ground up and move on to more complicated areas. You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and research experience.

Learn Neural Networks using Matlab Course [Size: 187 MB] ... MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. Although MATLAB is intended primarily for numerical computing, but by optional toolboxes, using the MuPAD symbolic engine, has access to symbolic computing capabilities too. One of these toolboxes is Neural Network toolbox. This toolbox is free, open source software for simulating models of brain and central nervous system, based on MATLAB computational platform. In these courses you will learn the general principles of Neural Network Toolbox designed in Matlab and you will be able to use this Toolbox efficiently as well.

The list of contents is: - Introduction: in this chapter the Neural Network Toolbox is Defined and introduced. An overview of neural network application is provided and the neural network training process for pattern recognition, function fitting and clustering data in demonstrated. - Neuron models:  A description of the neuron model is provided, including simple neurons, transfer functions, and vector inputs and single and multiple layers neurons are explained. The format of input data structures is very effective in the simulation results of both static and dynamic networks. So this effect is discussed in this chapter too. And finally the incremental and batch training rule is explained. - Perceptron networks: In this chapter the perceptron architecture is shown and it is explained how to create a perceptron in Neural network toolbox. The perceptron learning rule and its training algorithm is discussed and finally the network/Data manager GUI is explained. - Linear filters: in this chapter linear networks and linear system design function is discussed. The tapped delay lines and linear filters are discussed and at the end of the chapter LMS algorithm and linear classification algorithm used for linear filters are explained. - Backpropagation networks: The architecture, simulation, and several high-performance backpropagation training algorithms of backpropagation networks are discussed in this chapter. - Conclusion: in this chapter the memory and speed of different backpropagation training algorithms are illustrated. And at the end of the chapter all these algorithms are compared to help you select the best training algorithm for your problem in hand. - Matlab Software Installation: You are required to install the Matlab Software on your machine, so you can start executing the codes, and examples we work during the course.

Amos Gilat, Vish Subramaniam ... 480 pages - Publisher: Wiley; 1st edition (April, 2007) ... Language: English - ISBN-10: 0471734403 - ISBN-13: 978-0471734406 ...

Following a unique approach, this innovative book integrates the learning of numerical methods with practicing computer programming and using software tools in applications. It covers the fundamentals while emphasizing the most essential methods throughout the pages. Readers are also given the opportunity to enhance their programming skills using MATLAB to implement algorithms. They'll discover how to use this tool to solve problems in science and engineering. A clear and concise guide to numerical methods and their application: Mastering numerical methods has never been easier than with Gilat/Subramaniam’s Numerical Methods For Engineers and Scientists: An Introduction with Applications Using MATLAB. Uniquely accessible and concise, this book takes an innovative approach that integrates the study of numerical methods with hands-on programming practice using the popular MATLAB environment to solve realistic problems in engineering and science. Ideal for both students and professionals who would like to become more adept at numerical methods, Numerical Methods For Engineers and Scientists familiarizes you with: The mathematical background and fundamentals of numerical methods + Solving  nonlinear equations + Solving a system of linear equations + Eigenvalues and Eigenvectors + Function approximation, curve fitting, and interpolation + Differentiation + Integration + First-order and higher-order ODEs + Initial and boundary value problems. Using MATLAB’s built-in functions as tools for solving problems, you will practice applying numerical methods for analysis of real-world problems. All the information is presented in manageable, step-by-step fashion, supported by a large number of annotated examples and end-of-chapter problems. Lucid, carefully structured, and flexibly designed to fulfill a wide range of academic and practical needs, this book will help you develop the skills in numerical methods that will serve you well as a practicing engineer.

Amos Gilat ... 416 pages - Publisher: Wiley; 5th edition (January, 2014) ... Language: English- ISBN-10: 1118629868 - ISBN-13: 978-1118629864 ...

More college students use Amos Gilat’s MATLAB: An Introduction with Applications than any other MATLAB textbook. This concise book is known for its just-in-time learning approach that gives students information when they need it. The new edition gradually presents the latest MATLAB functionality in detail. Equally effective as a freshmen-level text, self-study tool, or course reference, the book is generously illustrated through computer screen shots and step-by-step tutorials, with abundant and motivating applications to problems in mathematics, science, and engineering.

Eugeniy E. Mikhailov ... 266 pages - Publisher: CRC Press; 1st edition (February, 2018) ... Language: English - ISBN-10: 1498738281 - ISBN-13: 978-1498738286 ...

This book offers an introduction to the basics of MATLAB programming to scientists and engineers. The author leads with engaging examples to build a working knowledge, specifically geared to those with science and engineering backgrounds. The reader is empowered to model and simulate real systems, as well as present and analyze everyday data sets. In order to achieve those goals, the contents bypass excessive "under the hood" details, and instead gets right down to the essential, practical foundations for successful programming and modeling. Readers will benefit from the following features: Teaches programming to scientists and engineers using a problem-based approach, leading with illustrative and interesting examples. + Emphasizes a hands-on approach, with "must know" information and minimal technical details. + Utilizes examples from science and engineering to showcase the application of learned concepts on real problems. + Showcases modeling of real systems, gradually advancing from simpler to more challenging problems. + Highlights the practical uses of data processing and analysis in everyday life.

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