MATLAB: Easy Way of Learning, covers exactly what students need to know in an introductory course. This comprehensive book helps reader in understanding all the aspects of MATLAB basics and applications in an easy way. The authors explain concepts by balanced treatment of theoretical and practical concepts with easy-to-understand programming codes and executions. The book is suitable for the postgraduate and undergraduate students of engineering and sciences streams.

The development of computer software for nonlinear control systems has provided many benefits for teaching, research, and the development of control systems design. MATLAB is considered the dominant software platforms for linear and nonlinear control systems analysis. This book provides an easy way to learn nonlinear control systems such as feedback linearization technique and Sliding mode control (Structure variable control) which are one of the most used techniques in nonlinear control dynamical systems; therefore teachers-students and researchers are all in need to handle such techniques; and since they are too difficult for them to handle such nonlinear controllers especially for a more complicated systems such as induction motor, satellite, and vehicles dynamical models. Thus, this document it is an excellent resource for learning the principle of feedback linearization and sliding mode techniques in an easy and simple way: Provides a briefs description of the feedback linearization and sliding mode control strategies Includes a simple method on how to determine the right and appropriate controller (P-PI-PID) for feedback linearization control strategy. A Symbolic MATLAB Based function for finding the feedback linearization and sliding mode controllers are developed and tested using several examples. A simple method for finding the approximate sliding mode controller parameters is introduced Where the program used to construct the nonlinear controller uses symbolic computations; such that the user should provide the program with the necessary functions f(x), g(x) and h(x) using the symbolic library.

MATLAB, which stands for MATrix LABoratory, is a state-of-the-art mathematical software package, which is used extensively in both academia and industry. It is an interactive program for numerical computation and data visualization, which along with its programming capabilities provides a very useful tool for almost all areas of science and engineering. Unlike other mathematical packages, such as MAPLE or MATHEMATICA, MATLAB cannot perform symbolic manipulations without the use of additional Toolboxes. It remains however, one of the leading software packages for numerical computation. The purpose of this tutorial is to familiarize the beginner to MATLAB, by introducing the basic features and commands of the program. It is in no way a complete reference and the reader is encouraged to further enhance his or her knowledge of MATLAB by reading some of the suggested references at the end of this guide.

This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014. Teaching and learning a substantial programming language aren’t always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

Classical Mechanics with MATLAB Applications is an essential resource for the advanced undergraduate taking introduction to classical mechanics. Filled with comprehensive examples and thorough descriptions, this text guides students through the complex topics of rigid body motion, moving coordinate systems, Lagrange's equations, small vibrations, and the special theory of relativity. Step-by-step illustrations and examples and computational physics tools further enhance learning and understanding by demonstrating accessible ways of obtaining mathematical solutions. In addition to the numerous examples throughout, each chapter contains a section of MATLAB code to introduce the topic of programming scripts and their modification for the reproduction of graphs and simulations.

Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Book Description: "ChemE Balancer: Guide to Balancing Chemical Equations" is the ultimate resource for science students seeking to conquer the challenges of balancing chemical equations. Whether you are a high school student just beginning your chemistry journey or a college student in need of a refresher, this comprehensive guide is designed to make the process accessible and easy to understand. With its user-friendly interface and step-by-step instructions, "ChemE Balancer" equips you with the knowledge and skills to confidently tackle even the most complex chemical equations. Say goodbye to confusion and frustration as you unravel the mysteries of balancing equations with a single click! Inside this book, you will find: 1. Clear Explanations: Understand the fundamental principles behind balancing chemical equations through concise and accessible explanations. Learn the significance of coefficients and subscripts and how they influence the equation's balance. 2. Step-by-Step Instructions: Follow along with detailed, step-by-step instructions that guide you through the process of balancing equations. Gain a solid foundation and build your skills as you progress through various examples. 3. Practice Exercises: Reinforce your understanding and sharpen your balancing skills with a wide range of practice exercises. From simple to challenging, these exercises provide ample opportunity to apply your knowledge and gain confidence. 4. Tips and Tricks: Discover helpful tips and tricks to overcome common challenges and pitfalls in balancing chemical equations. Master strategies to simplify complex equations and handle different types of reactions. Whether you are preparing for exams, completing assignments, or simply seeking a deeper understanding of chemical reactions, "ChemE Balancer" is your go-to guide. Empower yourself with the tools and knowledge to excel in chemistry and unleash your true potential. Don't let balancing chemical equations intimidate you any longer. With "ChemE Balancer," you will embark on a journey of mastery and confidently navigate the world of chemistry. Get ready to click your way to chemical balancing success!

A handbook for MATLAB which gives a focused approach to the software for students and professional researchers.

Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. Classification techniques predict categorical responses and Regression techniques predict continuous responses. This book develops Classification Techniques including Classification Support Vector Machine, Decision Trees, Logistic Regression, Discriminant Analysis, Nearest Neighbor Classifiers, Ensemble Classifiers, Naive Bayes, Pattern Recognition and Neural Networks for Classification.

Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.