Dynamical Systems with Applications using MATLAB Review

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(More customer reviews)I have been using the text for the last year or so. My familiarity with dynamical systems/non linear dynamics is over eight years now. I use this text as a reference for quick look-up on some of the more elementary techniques. Explanations are scarce and insufficient. Some minor inaccuracies are present, but can be easily disregarded if you are not particularly credulous/naive. You will have to consider Strogatz or J M T Thompson/H B Stewart texts if you want to understand what's happening physically. Or Nayfeh's body of texts on nonlinear physical systems for numerical techniques in simulating such systems or Phillip Holmes' and others for mathematical theory or geometric topology. But I love the book for its easily accessible presentation format for students and the succinctness of its prose. This book is certainly not for people from the mathematical side of dynamical systems, but great for undergrad or beginning grad level students in engineering or physics. This is mostly a cookbook, so don't expect brilliant flavors, just that you can put a meal on the table everyday.

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This introduction to dynamical systems theory guides readers through theory via example and the graphical MATLAB interface; the SIMULINK accessory is used to simulate real-world dynamical processes. Examples included are from mechanics, electrical circuits, economics, population dynamics, epidemiology, nonlinear optics, materials science and neural networks. The book contains over 330 illustrations, 300 examples, and exercises with solutions.

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Adaptive Filtering: Algorithms and Practical Implementation Review

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(More customer reviews)The book by Prof. Diniz is indeed amongst the best on adaptive signal processing. Most of the fundamental concepts are well explained, suitable examples are given and practical applications are also discussed. The chapter on adaptive IIR filters is unique and still cannot be found in any other book. Moreover, solutions to the problems can be obtained by ftp, which is something very useful for students. Despite this is an excellent book (5 star), the price is ridiculous, as occurs with most titles from this publisher. If I were Prof. Diniz I would change from Kluwer Academic Publishers to a more competitive publisher.

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This book presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner, using clear notations that facilitate actual implementation. Important algorithms are described in detailed tables which allow the reader to verify learned concepts. The book covers the family of LMS and algorithms as well as set-membership, sub-band, blind, IIR adaptive filtering, and more. Includes a CD supplement for instructors and students, offering lecture transparencies as well as MATLAB codes for all algorithms described in the text. The book is also supported by a web page maintained by the author.

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Modeling and Simulation In SIMULINK for Engineers and Scientists Review

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(More customer reviews)This book was clearly written by someone for whom English was not their first language. I admire the attempt -- I sincerely doubt I could write a book in a language that is not my own -- but the book definitely should have been read and tested by some technically-savvy individuals for whom English is their primary language. Since such testing apparently did not occur, this book is very difficult to read. I can only assume that the frequently-used statement of "without the loss of generosity" was actually supposed to be "without the loss of generality," as I never really expected Simulink to be generous.
The examples appear to be useful, but they are arranged in a confusing manner, making them hard to follow.
Compared to many Simulink books, this one is rather inexpensive. I'm currently wishing, however, that I'd splurged a bit more by purchasing a more expensive but, hopefully, better written book.

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The subject matter of this book is to present the procedural steps required for modeling and simulating the basic dynamic system problems in SIMULINK (a supplementary part of MATLAB) which follow some definitive model. However, the key features of the tex

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Introduction to FACTS Controllers: Theory, Modeling, and Applications (IEEE Press Series on Power Engineering) Review

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(More customer reviews)The disadvantages of the use of high power electronics apparatus in utility applications, I believe, are their high installation cost and the operating cost, especially, in terms of continuous power loss in the semiconductor devices. Therefore, I am always interested to know the theory of the lowest loss Voltage-Sourced Converters for industrial/utility applications. The detailed switching model in chapter 7 and the behavioral switching model in chapter 6 and the comparison with the behavioral average model in chapter 8 are exceptional. The book is written by Engineers for the Engineers. Great work!

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Demystifies FACTS controllers, offering solutions to power control and power flow problems

Flexible alternating current transmission systems (FACTS) controllers represent one of the most important technological advances in recent years, both enhancing controllability and increasing power transfer capacity of electric power transmission networks. This timely publication serves as an applications manual, offering readers clear instructions on how to model, design, build, evaluate, and install FACTS controllers. Authors Kalyan Sen and Mey Ling Sen share their two decades of experience in FACTS controller research and implementation, including their own pioneering FACTS design breakthroughs.
Readers gain a solid foundation in all aspects of FACTS controllers, including:

Basic underlying theories

Step-by-step evolution of FACTS controller development

Guidelines for selecting the right FACTS controller

Sample computer simulations in EMTP programming language

Key differences in modeling such FACTS controllers as the voltage regulating transformer, phase angle regulator, and unified power flow controller

Modeling techniques and control implementations for the three basic VSC-based FACTS controllers-STATCOM, SSSC, and UPFC

In addition, the book describes a new type of FACTS controller, the Sen Transformer, which is based on technology developed by the authors. An appendix presents all the sample models that are discussed in the book, and the accompanying FTP site offers many more downloadable sample models as well as the full-color photographs that appear throughout the book.
This book is essential reading for practitioners and students of power engineering around the world, offering viable solutions to the increasing problems of grid congestion and power flow limitations in electric power transmission systems.

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A First Course in Scientific Computing: Symbolic, Graphic, and Numeric Modeling Using Maple, Java, Mathematica, and Fortran90 Review

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(More customer reviews)Landau takes a refreshingly different approach to teaching students scientific computation. The field can be considered as two parts. One, the older and more heavily used, is about the "traditional" numerical analysis. You crunch numbers, and you get numbers out. The other approach is symbolic algebra.
Usually a text only deals with one type. Here, he teaches both. Plus, for each type, he offers the choice of two languages. For the numerical analysis, there is Fortran, version 90, and Java. While the symbolic algebra is performed using Mathematica or Maple. Ecumenical indeed!
These are excellent choices of languages. Fortran still dominates legacy numerical analysis, with massive libraries of subroutines that one has to work with or maintain. While Java lets the student learn good object oriented practices.
And Mathematica and Maple are perhaps the most common symbolic packages available.

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This book offers a new approach to introductory scientific computing. It aims to make students comfortable using computers to do science, to provide them with the computational tools and knowledge they need throughout their college careers and into their professional careers, and to show how all the pieces can work together. Rubin Landau introduces the requisite mathematics and computer science in the course of realistic problems, from energy use to the building of skyscrapers to projectile motion with drag. He is attentive to how each discipline uses its own language to describe the same concepts and how computations are concrete instances of the abstract.

Landau covers the basics of computation, numerical analysis, and programming from a computational science perspective. The first part of the printed book uses the problem-solving environment Maple as its context, with the same material covered on the accompanying CD as both Maple and Mathematica programs; the second part uses the compiled language Java, with equivalent materials in Fortran90 on the CD; and the final part presents an introduction to LaTeX replete with sample files.

Providing the essentials of computing, with practical examples, A First Course in Scientific Computing adheres to the principle that science and engineering students learn computation best while sitting in front of a computer, book in hand, in trial-and-error mode. Not only is it an invaluable learning text and an essential reference for students of mathematics, engineering, physics, and other sciences, but it is also a consummate model for future textbooks in computational science and engineering courses.

A broad spectrum of computing tools and examples that can be used throughout an academic career
Practical computing aimed at solving realistic problems
Both symbolic and numerical computations
A multidisciplinary approach: science + math + computer science
Maple and Java in the book itself; Mathematica, Fortran90, Maple and Java on the accompanying CD in an interactive workbook format

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Radar System Analysis and Modeling (Artech House Radar Library) Review

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(More customer reviews)It does not say so on the cover, but Barton points out in his Introduction that this book is actually the 3rd edition of a text, where the 2nd edition was published in 1988. The 3rd edition was motivated in no small part by advances in radar analysis and computing power since then.
As the book amply makes clear, radar analysis and modelling is very compute intensive. In 1988, some of the calculations might have necessitated one or more minicomputers. One attraction of the book is the lengthy set of nontrivial problems at the end of each chapter; which the reader is urged to tackle. Nowadays, these can be done on a personal computer, using some maths package. Hence the book's accompanying CD. However, instead of using Mathematica or Maple, Barton chose the rarer Mathsoft. The reason is that the intermediate steps are made available, so that you can easily modify these for your situations.
The radar applications in the text are heavily directed towards military usages. For historical reasons, and also because these tend to be the most technically demanding, in terms of rapid detection and identification. (See the enemy before he sees you.) With forays into Electronic Counter Measures and ECCM.
By the way, antenna design gets one lonely chapter. Perhaps somewhat cursory. But this subject is itself worthy of length monographs, and is not really the main topic here.
The level of discussion is clearly aimed at a engineer already in the field. A sophisticated, technically complex narrative. Showing as best as can be done on a declassified level, the limits of current analysis. A reader might reasonably ruminate that if this much can be revealed, what then is the true state of the art of military radar?

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A thorough update to the classic Modern Radar Systems Analysis, this reference is a comprehensive and cohesive introduction to radar systems design and performance estimation. It offers professionals the knowledge they need to specify, evaluate, or apply radar technology in civilian or military systems. This unique resource provides radar engineers with time-saving and effective techniques for their work in such defense-related applications as weapon systems design and electronic warfare. The book presents accurate detection range equations for realistically estimating radar performance in a variety of practical situations. As radar systems evolve, designers, engineers, and analysts can turn to this book again and again to calculate and evaluate systems performance to keep up with the latest advances in radar technology. CD-ROM Included! The accompanying disc contains example calculations, exercise problems, and analysis programs written in MathCad 11 and HTML.

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Simulation of Communication Systems: Modeling, Methodology and Techniques (Information Technology: Transmission, Processing and Storage) Review

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(More customer reviews)The second edition is a much-improved version compared to the first one. More details are added -- which makes it easy to follow. Anyone who is doing system simulation or performance analysis should have one around. I would have rated it a 5-star if the authors should have included some of the algorithms in a CD to save reader's time.

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Since the first edition of this book was published seven yearsago, the field of modeling and simulation of communication systems hasgrown and matured in many ways, and the use of simulation as aday-to-day tool is now even more common practice. With the currentinterest in digital mobile communications, a primary area ofapplication of modeling and simulation is now in wireless systems of adifferent flavor from the `traditional' ones. This second edition represents a substantial revision of the first,partly to accommodate the new applications that have arisen. Newchapters include material on modeling and simulation of nonlinearsystems, with a complementary section on related measurementtechniques, channel modeling and three new case studies; aconsolidated set of problems is provided at the end of the book.

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Cognitive Modeling Review

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(More customer reviews)This book is quite easy to follow and describes the technical and practical details how to actually do cognitive modeling!
Highly recommended for people who are interested in cognitive modeling but don't know where to start.

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Cognitive Modeling is the first book to provide students with an easy-to understand introduction to the basic methods used to build and test cognitive models. Authors Jerome R. Busemeyer and Adele Diederich answer many of the questions that researchers face when beginning work on cognitive models, such as the following: What makes a cognitive model different from conceptual or statistical models? How do you develop such a model? How can you derive qualitatively different predictions between two cognitive models? Focusing on a few key representations, the authors introduce a basic problem in each chapter, illustrate the concept with three examples, and end with a summary of general principles, making this book by far the most accessible cognitive modeling book on the market. Key Features

Emphasizes modeling by presenting the tools needed to build a cognitive model, rather than simply reviewing existing models of cognition
Provides tutorial presentations of psychological, mathematical, statistical, and computational methods used in all areas of cognitive modeling
Includes detailed examples applied to real cognitive models published in the literature in a variety of areas, including recognition, categorization, decision making, and learning
Stresses the importance of designing the right conditions for evaluating models
Addresses the issues of individual differences in cognitive modeling head-on

Cognitive Modeling is ideal for students and researchers across the various domains of cognitive sciences, including perception, learning, decision making, and inference. It is intended for use in upper-level undergraduate and graduate courses such as Cognition/Cognitive Modeling, Cognitive Science, Cognitive Psychology, Quantitative Methods, and Mathematical Modeling in Psychology.

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Antenna and EM Modeling with Matlab Review

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(More customer reviews)The book is well written but not very extensive. It points to the applications right from the beggining and it is certainly of great value for students and engineers already engagged with Balanis Book on antenna theory. I strongly recommend Makarov's book for training students with a strong numerical methods background on electromagnetics, as a prerequisite before taking the course.

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An accessible and practical tool for effective antenna designDue to the rapid development of wireless communications, the modeling of radiation and scattering is becoming more important in the design of antennas. Consequently, it is increasingly important for antenna designers and students of antenna design to have a comprehensive simulation tool.Sergey Makarov's text utilizes the widely used Matlab(r) software, which offers a more flexible and affordable alternative to other antenna and electromagnetic modeling tools currently available. After providing the basic background in electromagnetic theory necessary to utilize the software, the author describes the benefits and many practical uses of the Matlab package. The text demonstrates how Matlab solves basic radiation/scattering antenna problems in structures that range from simple dipoles to patch antennas and patch antenna arrays. Specialized antenna types like fractal antennas and frequency selective surfaces are considered as well. Finally, the text introduces Matlab applications to more advanced problems such as broadband and loaded antennas, UWB pulse antennas, and microstrip antenna arrays.For students and professionals in the field of antenna design, Antenna and EM Modeling with Matlab:* Strikes an important balance between text and programming manual* Provides numerous examples on how to calculate important antenna/target parameters* Provides means for modifying existing codes for various individual projects* Includes a CD-ROM with Matlab codes and antenna geometry files
The present MATLAB codes are only supported by MATLAB 5 and 6 (up to 2004).

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Tutorial on Neural Systems Modeling Review

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(More customer reviews)We used this book for the upper-level undergraduate students in an interdisciplinary computational neuroscience course at a small liberal arts college. This is a fine, well-written book. One of the strengths of this book is that it starts with very basic programming in Matlab, so that the students without programming backgrounds can easily follow along. The programs in the book are well commented, and they progress slowly and logically in complexity. More advanced math topics are well separated out in Math Boxes. The examples of the neural systems are covered in enough (but not too much) details to be interesting and accessible to the readers. The book is definitely considerate of and sensitive to the wonderfully interdisciplinary nature of this field, so that the materials can be digested by people with different backgrounds. One can not contain all the topics in computational neuroscience in a single book, but this book does a great job of covering many important and interesting ideas/areas (Hebbian learning, Hopfield model, lateral inhibition, adaptation, supervised and unsupervised learning, etc.).
It works very well as an introductory textbook (or tutorial) of the field. The codes and the discussions are clear and simple (not intended as an advanced textbook), and to me, that's the strength and unique quality of this book.
By the way, most of the computer programs listed in the book work well with Octave (as well as Matlab).

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Neural systems models are elegant conceptual tools that provide satisfying insight into brain function. The goal of this new book is to make these tools accessible. It is written specifically for students in neuroscience, cognitive science, and related areas who want to learn about neural systems modeling but lack extensive background in mathematics and computer programming.The book opens with an introduction to computer programming. Each of twelve subsequent chapters presents a different modeling paradigm by describing its basic structure and showing how it can be applied in understanding brain function. The text guides the reader through short, simple computer programs printed in the book and available by download at the companion website that implement the paradigms and simulate real neural systems. Motivation for the simulations is provided in the form of a narrative that places specific aspects of neural system behavior in the context of more general brain function. The narrative integrates instruction for using the programs with description of neural system function, and readers can actively experience the fun and excitement of doing the simulations themselves. Designed as a hands-on tutorial for students, this book also serves instructors as both a teaching tool and a source of examples and exercises that provide convenient starting points for more in-depth exploration of topics of their own specific interest.The distinguishing pedagogical feature of this book is its computer programs, written in MATLAB, that help readers develop basic skill in the area of neural systems modeling. (All of the program files are available online via the book s companion website. Actual data on real neural systems is presented in the book for comparison with the results of the simulations. Also included are asides ( Math Boxes ) that present mathematical material that is relevant but not essential to running the programs. Exercises and references at the end of each chapter invite readers to explore each topic area on their own.

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Modeling Risk, + DVD: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, and Portfolio Optimization (Wiley Finance) Review

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(More customer reviews)Dr Mun's books are always very insightful with lots of practical examples and tools. A very good book for the professional that needs an in depth understanding of risk management. The book provides many analytical tools to properly assess and mitigate risks!


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An updated guide to risk analysis and modeling
Although risk was once seen as something that was both unpredictable and uncontrollable, the evolution of risk analysis tools and theories has changed the way we look at this important business element. In the Second Edition of Analyzing and Modeling Risk, expert Dr. Johnathan Mun provides up-to-date coverage of risk analysis as it is applied within the realms of business risk analysis and offers an intuitive feel of what risk looks like, as well as the different ways of quantifying it.
This Second Edition provides professionals in all industries a more comprehensive guide on such key concepts as risk and return, the fundamentals of model building, Monte Carlo simulation, forecasting, time-series and regression analysis, optimization, real options, and more.
Includes new examples, questions, and exercises as well as updates using Excel 2007
Book supported by author's proprietary risk analysis software found on the companion CD-ROM
Offers both a qualitative and quantitative description of risk

Filled with in-depth insights and practical advice, this reliable resource covers all of the essential tools and techniques that risk managers need to successfully conduct risk analysis.
Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

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Modeling Derivatives Applications in Matlab, C++, and Excel Review

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(More customer reviews)I highlight two points:
1. The inclusion of Matlab and Excel code in almost all topics of the book.
2. All the content is new and more advanced, there is no recovered topics of his previous book.


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Prebuilt Code for Modeling and Pricing Today's Complex DerivativesJustin London shows how to implement pricing algorithms for a wide variety of complex derivatives, including rapidly emerging instruments covered in no other book. Utilizing actual Bloomberg data, London covers credit derivatives, CDOs, mortgage-backed securities, asset-backed securities, fixed-income securities, and today's increasingly important weather, power, and energy derivatives.His robust models are designed for both ease of use and ease of adaptation, and may be downloaded by the book's purchasers from a secured Web site.Modeling Derivatives Applications in Matlab, C++, and Excel will be indispensable to sell-side professionals who model derivatives; buy-side professionals who must understand the derivatives offered to them; experienced quants; developers at Wall Street firms; and any financial engineering practitioner or student entering the derivatives field for the first time.Presents broader coverage and more models than any competitive book Covers everything from swaps to interest rate models, mortgage- and asset-backed securities to the HJM model Includes code for all three leading derivatives development platforms The only book to present models for Matlab, C++, and Excel Addresses the fastest-growing areas of derivatives development Includes models for weather, power, and energy derivatives, CDOs, and more Contains extensive real-world examples. Theentire book utilizesMatlab, C++, and Excel. Users need Matlab installed, Visual C++, and Excel.In addition, some examples using Matlab toolkits are used: Chapter 1makes use of the Fixed-Income Toolkit. Appendix A makes use of the Financial Derivatives Toolkit and Matlab Excel Link.These toolkits do not come with the book, but can be obtained from Mathworks.Downloadable models available ONLY to purchasers of this book.Purchasers receive a unique access code enabling secure access to downloadable, prebuilt code and templates for Matlab, C++, and Excel. Preface xv Acknowledgments xix About the Author xxiChapter 1Swaps and Fixed Income Instruments 1 Chapter2 Copula Functions 67Chapter3 Mortgage-Backed Securities 91Chapter4 Collateralized Debt Obligations 163Chapter5 Credit Derivatives 223Chapter6Weather Derivatives 299Chapter7 Energy and Power Derivatives 333Chapter8 Pricing Power Derivatives: Theory and Matlab Implementation 407Chapter9 Commercial Real Estate Asset-Backed Securities 447Appendix AInterest Rate Tree Modeling in Matlab 473Appendix BChapter 7 Code 503 References 543 Index 555

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Introduction to Computational Science: Modeling and Simulation for the Sciences Review

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(More customer reviews)This is a great book for the college bound. Very interesting and easy to read.

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Computational science is a quickly emerging field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. However, limited educational materials exist in this field. Introduction to Computational Science fills this void with a flexible, readable textbook that assumes only a background in high school algebra and enables instructors to follow tailored pathways through the material. It is the first textbook designed specifically for an introductory course in the computational science and engineering curriculum.

The text embraces two major approaches to computational science problems: System dynamics models with their global views of major systems that change with time; and cellular automaton simulations with their local views of how individuals affect individuals. While the text is generic, an extensive author-generated Web-site contains tutorials and files in a variety of software packages to accompany the text.

Generic software approach in the text
Web site with tutorials and files in a variety of software packages
Engaging examples, exercises, and projects that explore science
Additional, substantial projects for students to develop individually or in teams
Consistent application of the modeling process
Quick review questions and answers
Projects for students to develop individually or in teams
Reference sections for most modules, as well as a glossary
Online instructor's manual with a test bank and solutions

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Simulation and Optimization in Finance + Website: Modeling with MATLAB, @Risk, or VBA (Frank J. Fabozzi Series) Review

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(More customer reviews)Prof.Pachamanova has written one of the best introductions to Simulation and Optimization methods in finance. This book provides a strong theoretical foundation and the website provides a lot of cases and useful hands-on exercises to apply and understand the concepts explained in the book. This book is highly recommended for business and engineering students who are interested in a career in the quantitative finance industry. This book is also recommended to new entrants to the quant finance industry and to financial practitioners who primarily use Excel for quantitative modeling but are interested in building more rigorous models using VBA, @RISK and MATLAB.
This book has the optimal combination of theory and practice. It starts out with a through introduction to statistics, finance and optimization concepts. In the second part, the book describes portfolio optimization theory and applications in equity and fixed income markets. The third part focuses on asset pricing models discussing classical and dynamic models. The fourth section mainly focuses on derivative pricing and provides a very good introduction to Monte-Carlo simulation methods. Topics of current interest such as pricing MBS products are also described in this section. Part five focuses on capital budget decisions and has a very good introduction to real options. The Software hints in each chapter and the supplementary materials on the website help students and practitioners to immediately try out examples and fortify their knowledge.
Prof.Pachamanova's didactic approach and the vast coverage of topics makes this book a must have for new quantitative analysts, business students and engineers interested in a career in finance. As a Financial Modeling consultant who works at MathWorks (the maker of MATLAB), I get a lot of questions on recommendations for books to apply financial theory using computational tools. This book is a gem and would makes great addition to your quantitative investing library.
Full Disclosure: I took a Statistics class with Prof.Pachamanova during my MBA program at Babson College

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An introduction to the theory and practice of financial simulation and optimization
In recent years, there has been a notable increase in the use of simulation and optimization methods in the financial industry. Applications include portfolio allocation, risk management, pricing, and capital budgeting under uncertainty.
This accessible guide provides an introduction to the simulation and optimization techniques most widely used in finance, while at the same time offering background on the financial concepts in these applications. In addition, it clarifies difficult concepts in traditional models of uncertainty in finance, and teaches you how to build models with software. It does this by reviewing current simulation and optimization methodology-along with available software-and proceeds with portfolio risk management, modeling of random processes, pricing of financial derivatives, and real options applications.
Contains a unique combination of finance theory and rigorous mathematical modeling emphasizing a hands-on approach through implementation with software
Highlights not only classical applications, but also more recent developments, such as pricing of mortgage-backed securities
Includes models and code in both spreadsheet-based software (@RISK, Solver, Evolver, VBA) and mathematical modeling software (MATLAB)

Filled with in-depth insights and practical advice, Simulation and Optimization Modeling in Finance offers essential guidance on some of the most important topics in financial management.

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