Articles by "MATLAB"

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Lisa Oberbroeckling ... 296 pages - Publisher: Academic Press; (June, 2020) ... Language: English - ISBN-10: 0128177993 - ISBN-13: 978-0128177990.

Providing an alternative to engineering-focused resources in the area, Programming Mathematics Using MATLAB® introduces the basics of programming and of using MATLAB® by highlighting many mathematical examples. Emphasizing mathematical concepts through the visualization of programming throughout the book, this useful resource utilizes examples that may be familiar to math students (such as numerical integration) and others that may be new (such as fractals). Additionally, the text uniquely offers a variety of MATLAB® projects, all of which have been class-tested thoroughly, and which enable students to put MATLAB® programming into practice while expanding their comprehension of concepts such as Taylor polynomials and the Gram–Schmidt process. Programming Mathematics Using MATLAB® is appropriate for readers familiar with sophomore-level mathematics (vectors, matrices, multivariable calculus), and is useful for math courses focused on MATLAB® specifically and those focused on mathematical concepts which seek to utilize MATLAB® in the classroom.

Michael Paluszek, Stephanie Thomas ... 252 pages - Publisher: Apress; (February, 2020) ... Language: English - ISBN-10: 1484251237 - ISBN-13: 978-1484251232.

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images.

Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will Learn: Explore deep learning using MATLAB and compare it to algorithms + Write a deep learning function in MATLAB and train it with examples + Use MATLAB toolboxes related to deep learning + Implement tokamak disruption prediction.

MathWorks MATLAB R2020a v9.8.0.1380330 + Update 2 [Size: 20.8 GB] ... MATLAB is a highlevel language and interactive environment that is used by millions of engineers and scientists around the world. It allows you to explore and visualize ideas and collaborate in various disciplines, including signal and image processing, communications, management systems and financial engineering. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB is designed for the way you think and the work you do. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Features of Mathworks Matlab R2020a v9.8: Perform a variety of complex mathematical calculations and heavy + Development environment for managing code, files, and data + Explore ways to achieve this solution + A variety of mathematical functions for linear algebra, statistics, Fourier analysis, optimization, filtering, numerical integration and … + Drawing two-dimensional and three-dimensional graphics functions for visualizing data as + Design and construction of user interfaces under the programming languages ​​C ++, C or Java + Ability to test and measure the exact functions and graphs + The possibility of communication signal processing, image and video + There are various Jbhabzarhay engineering companies for specific applications such as + Telecommunications, control, fuzzy, estimates, statistics, data collection, simulation systems.

Svein Linge, Hans Petter Langtangen ... 216 pages - Publisher: Springer; (August, 2016) ... Language: English - AmazonSIN: B078YHJHL2.

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

Mircea Ancau ... 295 pages - Publisher: Cambridge Scholars Publishing; (November, 2019) ... Language: English - ISBN-10: 1527538494 - ISBN-13: 978-1527538498.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems: This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

B. S. Grewal ... 1597 pages - Publisher: Mercury Learning and Information; (September, 2018) ... Language: English - AmazonSIN: B07HHJ611G.

This book is intended as an introduction to numerical methods for scientists and engineers. Providing an excellent balance of theoretical and applied topics, it shows the numerical methods used with C, C++, and MATLAB.

Brief Table of Contents: 1: Approximations and Errors in Computation. 2: Solution of Algebraic and Transcendental Equations. 3: Solution of Simultaneous Algebraic Equations. 4: Matrix Inversion and the Eigenvalue Problem. 5: Empirical Laws and Curve-Fitting. 6: Finite Differences. 7: Interpolation. 8: Numerical Differentiation and Integration. 9: Difference Equations. 10: Numerical Solution of Ordinary Differential Equations. 11: Numerical Solution of Partial Differential Equations. 12: Linear Programming. 13: A Brief Review of Computers. 14: Numerical Methods Using C. 15: Numerical Methods Using C++. 16: Numerical Methods Using MATLAB. Appendix A: Useful Information. Appendix B: Answers to Exercises. Appendix C: Bibliography. Index.

Edward B. Magrab, Shapour Azarm ... 848 pages - Publisher: Prentice Hall; 3rd edition (January, 2010) ... Language: English - ISBN-10: 0131991108 - ISBN-13: 978-0131991101.

An Engineer's Guide to MATLAB, is an authoritative guide to generating readable, compact, and verifiably correct MATLAB programs. It is ideal for undergraduate engineering courses in Mechanical, Aeronautical, Civil, and Electrical engineering that require/use MATLAB. This highly respected guide helps students develop a strong working knowledge of MATLAB that can be used to solve a wide range of engineering problems. Since solving these problems usually involves writing relatively short, one-time-use programs, the authors demonstrate how to effectively develop programs that are compact yet readable, easy to debug, and quick to execute. Emphasis is on using MATLAB to obtain solutions to several classes of engineering problems, so technical material is presented in summary form only.

E. S. Gopi ... 256 pages - Publisher: Springer; (October, 2019) ... Language: English - ASIN: B07Z9MS1BZ by Amazon.

This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques.

Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.

Isacco Arnaldi ... 264 pages - Publisher: Springer; (July, 2018) ... Language: English - ISBN-10: 331991538X - ISBN-13: 978-3319915388.

This textbook is intended for a semester-length course in Sigma-Delta converters. The author minimizes his use of mathematical theory, emphasizes real-use cases, and discuses concepts in a way to be accessible to inexperienced students and entry-level, practicing engineers. Little or no prior knowledge of Sigma-Delta converters and/or MATLAB/Simulink is assumed. Readers will learn what the design process involves, the trade-offs to consider, how a modulator is actually simulated and how to consider a specific design successful. Each chapter is begins with the essential, practical information, while the necessary, theoretical concepts are presented through results evaluation of the suggested simulation exercises of the modulators supplied in the MATLAB/Simulink Toolbox software accompanying this book.

Zekai Sen ... 277 pages - Publisher: Springer; (March, 2019) ... Language: English - ASIN: B07Q7NZPRR by Amazon.

This book is designed to provide easy means of problem solving based on the science philosophical and logical rules that lead to effective and reliable software at the service of professional earth system scientists through numerical scientific computation techniques. Through careful examination of software illuminated by brief scientific explanations given in the book the reader may develop his/her skills of computer program writing. Science aspects that are concerned with earth systems need numerical computation procedures and algorithms of data collected from the field measurements or laboratory records. The same is also valid for data processing in social sciences and economics. Some of the data assessment and processing procedures are at the large scales and complex, and therefore, require effective and efficient computer programs. Data reduction and graphical display in addition to probabilistic and statistical calculations are among the general purposes of the book. Not only students’ works but also projects of researchers at universities and tasks of experts in different companies depend on reliable software. Especially, potential users of MATLAB in earth systems need a guidance book that covers a variety of practically applicable software solutions.

Rajesh Kumar Arora ... 466 pages - Publisher: Chapman and Hall/CRC; (May, 2015) ... Language: English - ISBN-10: 1498721125 - ISBN-13: 978-1498721127.

Choose the Correct Solution Method for Your Optimization Problem: Optimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden–Fletcher–Goldfarb–Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible directions, genetic algorithms, particle swarm optimization (PSO), simulated annealing, ant colony optimization, and tabu search methods. The author shows how to solve non-convex multi-objective optimization problems using simple modifications of the basic PSO code. The book also introduces multidisciplinary design optimization (MDO) architectures―one of the first optimization books to do so―and develops software codes for the simplex method and affine-scaling interior point method for solving linear programming problems. In addition, it examines Gomory’s cutting plane method, the branch-and-bound method, and Balas’ algorithm for integer programming problems. The author follows a step-by-step approach to developing the MATLAB® codes from the algorithms. He then applies the codes to solve both standard functions taken from the literature and real-world applications, including a complex trajectory design problem of a robot, a portfolio optimization problem, and a multi-objective shape optimization problem of a reentry body. This hands-on approach improves your understanding and confidence in handling different solution methods. The MATLAB codes are available on the book’s CRC Press web page.

Mathworks Matlab R2019b v9.7.0 build 1261785 + All UpDates [Size: 21.5 GB] ... MATLAB is a highlevel language and interactive environment that is used by millions of engineers and scientists around the world. It allows you to explore and visualize ideas and collaborate in various disciplines, including signal and image processing, communications, management systems and financial engineering. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB is designed for the way you think and the work you do. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Features of Mathworks Matlab R2019: Organize live scripts using additional subheading styles + Navigate within a live script using internal hyperlinks + Create new and open existing live scripts faster + Use string arrays in MATLAB, Simulink, and Stateflow + Improved performance of set operations with enumerations + Create a polyshape object from a 2-D triangulation + Generate random numbers using Threefry and Philox algorithms + Customize node and edge labels with font properties + Explore data with panning, zooming, data tips, and 3-D rotation enabled by default + Visualize grouped data as a scatter plot with marginal histograms + Generate improved code when importing from spreadsheets + Write tall arrays in a variety of formats to local or remote locations + Transform table or timetable data by groups + Add and configure date selection components on the App Designer canvas + Pass multidimensional numeric or logical arrays between MATLAB and Python.

Yogesh Jalria ... 614 pages - Publisher: CRC Press; 3rd edition (September, 2019) ... Language: English - ISBN-10: 1498778232 - ISBN-13: 978-1498778237.

Design and Optimization of Thermal Systems, Third Edition: with MATLAB® Applications provides systematic and efficient approaches to the design of thermal systems, which are of interest in a wide range of applications. It presents basic concepts and procedures for conceptual design, problem formulation, modeling, simulation, design evaluation, achieving feasible design, and optimization. Emphasizing modeling and simulation, with experimentation for physical insight and model validation, the third edition covers the areas of material selection, manufacturability, economic aspects, sensitivity, genetic and gradient search methods, knowledge-based design methodology, uncertainty, and other aspects that arise in practical situations. This edition features many new and revised examples and problems from diverse application areas and more extensive coverage of analysis and simulation with MATLAB®.

Abdulhamit Subasi ... 456 pages - Publisher: Academic Press; (March, 2019) ... Language: English - ISBN-10: 0128174447 - ISBN-13: 978-0128174449.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.

Michael Paluszek, Stephanie Thomas ... 368 pages - Publisher: Apress; 2nd edition (February, 2019) ... Language: English - ISBN-10: 1484239156 - ISBN-13: 978-1484239155. 

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.

What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLAB + How these three areas complement each other + How these three areas are needed for robust machine learning applications + How to use MATLAB graphics and visualization tools for machine learning + How to code real world examples in MATLAB for major applications of machine learning in big data. Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

G. A. Vijayalakshmi Pai ... 316 pages - Publisher: Wiley-ISTE; (December 27, 2017) ... Language: English - ASIN: B078X252PR by Amazon.

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.

Rudra Pratap ... 288 pages - Publisher: Oxford Univ. Press; (November, 2009) ... Language: English - ISBN-10: 0199731241 - ISBN-13: 978-0199731244.

MATLAB, a software package for high-performance numerical computation and visualization, is one of the most widely used tools in the engineering field today. Its broad appeal lies in its interactive environment, which features hundreds of built-in functions for technical computation, graphics, and animation. In addition, MATLAB provides easy extensibility with its own high-level programming language. Enhanced by fun and appealing illustrations, Getting Started with MATLAB employs a casual, accessible writing style that shows users how to enjoy using MATLAB.

Features: * Discusses new features and applications, including the new engine of symbolic computation in MATLAB 7.8 (released March 2009) * Provides two sets of self guided tutorials for learning essential features of MATLAB * Includes updated commands, examples, figure, and graphs * Familiarizes users with MATLAB in just a few hours though self-guided lessons * Covers elementary, advanced, and special functions * Supplements any course that uses MATLAB * Works as a stand-alone tutorial and reference.

William Menke, Joshua Menke ... 342 pages - Publisher: Academic Press; 2nd edition (March, 2016) ... Language: English - ISBN-10: 0128044888 - ISBN-13: 978-0128044889.

Environmental Data Analysis with MatLab is a new edition that expands fundamentally on the original with an expanded tutorial approach, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. Since publication of the bestselling Environmental Data Analysis with MATLAB®, many advances have been made in environmental data analysis. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often noisy data drawn from a broad range of sources. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios.

MoonJung Cho, Wendy L. Martinez ... 286 pages - Publisher: Chapman and Hall/CRC; (December, 2014) ... Language: English - ISBN-10: 1466596562 - ISBN-13: 978-1466596566

Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book: Covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB * Presents examples of how MATLAB can be used to analyze data * Offers access to a companion website with data sets and additional examples * Contains figures and visual aids to assist in application of the software *Explains how to determine what method should be used for analysis. Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.

Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka ... 616 pages - Publisher: Chapman and Hall/CRC; 3rd edition (August, 2017) ... Language: English - ASIN: B074MVTKM9 by Amazon

Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website.

New to the Third Edition: Random projections and estimating local intrinsic dimensionality. + Deep learning autoencoders and stochastic neighbor embedding. + Minimum spanning tree and additional cluster validity indices. + Kernel density estimation. + Plots for visualizing data distributions, such as beanplots and violin plots. + A chapter on visualizing categorical data.

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