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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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 and Simulation of Dynamic Systems Review

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(More customer reviews)I have taken the Modeling course at UT Arlington with Dr. Woods (the author), the book covers modeling techniques for Electrical, mechanical, heat tranfer..systems. it has a practical review of differenitial equations. Also has Practical simulation information. and it is great to keep as a reference.

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This book reflects the state-of-the-art and current trends in modeling and simulation. The book provides comprehensive coverage of 1) the modeling techniques of the major types of dynamic engineering systems, 2) the solution techniques for the resulting differential equations for linear and nonlinear systems, and 3) the attendant mathematical procedures related to the presentation of dynamic systems and determination of their time and frequency response characteristics. It explains in detail how to select all of the system component parameter values for static and dynamic performance specifications and limits. For anyone interested in systems dynamics, modeling, and interdisciplinary systems.

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Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology) Review

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(More customer reviews)Regulatory networks are central to every aspect of computational biology. Determining what they are, and what genes, proteins, and post-translational modifications interact is a major and exciting field of study.
I just didn't come away from this book with that excitement. I was hoping for more about the large-scale regulation networks, but these papers go down to the quantum mechanics of interactions between pairs of molecules. I appreciate that the exact interactions matter, and that computation is probably the only way to examine some kinds of interactions (e.g. the ones in lethal mutations). It's just not what I think of as a "network."
I was also hoping for some more specifics about the computation techniques. There were some interesting insights here. For example, I never thought about the similarities between steady state chemical equilibrium and steady state Markov model behavior before, but the formalisms have striking similarities. I was also interested in some of the information-based measures for determining how well a model represents a system. I learned that the statistical assumptions behind normal chemical "equilibrium" break down at the scale of bacteria - instead, presence or absence of individual molecules matters more. Still, those were isolated kinds of facts and never came together into a whole for me.
The range of views was worthwhile. On the whole, though, the models all seemed very low-level to me, probably not well suited to handling more than a few dozen interactions, and the computation specifics were not always explicit. I'm still looking for a book with more information that I can apply directly.

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The advent of ever more sophisticated molecular manipulation techniqueshas made it clear that cellular systems are far more complex and dynamic thanpreviously thought. At the same time, experimental techniques are providing analmost overwhelming amount of new data. It is increasingly apparent that linkingmolecular and cellular structure to function will require the use of newcomputational tools.This book provides specific examples, across a wide range ofmolecular and cellular systems, of how modeling techniques can be used to explorefunctionally relevant molecular and cellular relationships. The modeling techniquescovered are applicable to cell, developmental, structural, and mathematical biology;genetics; and computational neuroscience. The book, intended as a primer for boththeoretical and experimental biologists, is organized in two parts: models of geneactivity and models of interactions among gene products. Modeling examples areprovided at several scales for each subject. Each chapter includes an overview ofthe biological system in question and extensive references to important work in thearea.

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Mathematics for Dynamic Modeling, Second Edition Review

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(More customer reviews)This is not a new cover on an older book. Having enjoyed Beltrami's first book I was glad to see a second. The new continues the subject of modeling not math. Well written, the author's book ties the conceptual difficulites of the subjects with the necessary math to get the point accross and guide the reader to new frontiers of insight again in the sense of the physical model not the math. This is a very important point! One does not lose sight of the over-all objective as with some math intensive proof types. The beauty of the work is getting the necessary across with the least. One disappointment though. The book was to short. I hope the author continues another work. Perhaps some more indepth of previous covered material. All examples and problems are easily solved in Mathcad, which already has the depth but sometimes not the explanation.

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