Adaptive Filter Theory (4th Edition)

Customer Reviews:

It's exactly what the title states, "Adaptive Filter THEORY".......2005-03-17

I was introduced to this text in a graduate course. I was not too thrilled about learning from another Haykin book due to a previous experience with his Communication Systems text in an undergraduate course (Horribly confusing... Proakis's text is infinitely better). To my surprise, the book was very detailed and easy to read. The math is very clear and detailed (great for the self learner). Also, the second chapter, which serves as a review of stationary processes and properties, was written much better than most random process textbooks (I applaud Haykin for this given the section was only a review). In chapter 3 or 4, he shows the derivation of the Levison-Durbin Algorithm step-by-step. I strongly disagree with some of the other reviews stating this text is just the typical engineering manual or cookbook with no explainations.

However, this is either a love or hate text. If you are looking for a text about practical linear predictive filter design, this is NOT the book for you. This text is heavily geared towards understanding the theory behind the design... hence the title Adaptive Filter THEORY. However, it can make a great reference to engineers in the field of DSP.

Not a bad reference book........2004-06-17

This book looks very impressive, but if you try to understand it you'll find it very mechanical. There is not much motivation behind the many pages of formulas and derivations. I'm not even sure how many people actually read those derivations becuase even in its 4th edition the book and its solution manual both have many typos (see, for example, equations 8.11 and 12.5). Even the problems are more focused on derivations than on numerical examples. This is a good cookbook if you just want to implement an algorithm or find some pointers to the original research papers. Like many other reviewers, I beleive that engineering textbooks are losing their depth and becoming more and more like instruction manuals.

A very good book for Adaptive DSP..........2001-05-09

I have always wondered why many people have negative opinions about books by Simon Haykin, whether it is 'Communication Systems' or 'Adaptive Filter Theory'. Particularly, this book 'Adaptive Filter Theory', in my opinion, is one of the bestbooks on this subject. As Julius Kusuma correctly mentioned, this book is indeed an "adventure ride" into the field of Adaptive Filter Theory.

I discovered this book when I was doing a class project on Self-Orthogonalizing algorithms for Adaptive Beamforming and I felt that all the relevant information that I needed was present in this book. I did'nt really feel the neccesity to refer anything outside this book.

Apart from that, this book contains everything that a graduate student needs to know about this exciting field of adaptive filters. The author assumes some background on Random Signal Theory... I'd suggest to look up Sam Shanmugan et al's, "Random Signals: Detection, Estimation and Data Analysis" before beginning to read (enjoy) this "adventure ride" on Adaptive Filters.

Adventures in the development of stochastic DSP.......2000-07-24

Despite the commonly negative opinion against Simon Haykin's book, I find this book to be a very fun reading. It starts off with a very brief review of DSP (more useful just for getting familiar with the notation, really), properties of random processes, and a small section on linear algebra in the middle of the book.

The rest of the book can be viewed as a story of how different approaches and algorithms were developed, and is a little difficult to use as reference due to its lack of structure and over-dependency on the previous chapters, both for technical content and notation. I have to admit that the notation used in this book is very, very poor and can be a source of frustration. The dependency is also a pain because you always have to keep flipping 100 pages back because Mr. Haykin prefers to say "Eqn. (4.24)" instead of "an AR model".

But there's a lot of hidden treasures within this book that should have been more emphasized. For example, Mold's theorem that states that any discrete stationary process can be decomposed into a deterministic component and a random component, which are uncorrelated to each other. I'm sorry, but a reference to a proof in another book is not enough to really motivate me. This is a very fundamental theorem if you're interested in stochastic signal processing. Sure, you don't cover the Fundamental Theorem of Calculus in your very first calculus class, but then again this is supposed to be a fairly advanced book.

Adventures in the development of stochastic DSP.......2000-07-24

Book Description

This book is devoted to local and global analysis of weakly connected systems with applications to neurosciences. Using bifurcation theory and canonical models as the major tools of analysis, it presents systematic and well motivated development of both weakly connected system theory and mathematical neuroscience. Bifurcations in neuron and brain dynamics, synaptic organizations of the brain, and the nature of neural codes are among the many important issues addressed. The authors offer the reader classical results as well as some of the most recent developments in the field. The book will be useful to researchers and graduate students in various branches of mathematical neuroscience.

Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics

Average customer rating:

The only book on this important subject in print

Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Henry Stark , and Yongyi Yang
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)

ASIN: 0471241407

Book Description

A guide to the theory and application of methods of projections.

With the rise of powerful personal computers, methods of vector space projections have moved rapidly from the realm of theory into widespread use. This book reflects the growing interest in the application of these methods to problem solving in science and engineering. It brings together material previously scattered in disparate papers, book chapters, and articles, and offers a systematic treatment of vector space projections.

Written by two leading authorities in the field, this self-contained volume provides a tutorial on projection methods and how to apply them in science and engineering. It details effective problem-solving strategies, and explores key applications in communication and signal processing, neural networks and pattern recognition, and optics and image processing. This book:

Reviews the fundamentals of vector space theory
Covers principles and applications of vector space projections in general, and projections onto convex sets in particular
Provides real-world examples solvable on PCs and modest workstations
Features more than 100 illustrations
Includes end-of-chapter exercises and references.

This extremely useful reference for practicing engineers, scientists, and educators can also be used for graduate-level study in science, mathematics, and engineering. Portions of the book have been used as material in short courses on applications of vector space projections.

Customer Reviews:

The only book on this important subject in print.......1999-11-18

This book showed me how vector space methods are a natural way to use the computer for solving difficult problems in engineering design. Although my background in mathematics consists only of advanced calculus and linear algebra, I had no trouble following the theory and learning the applications. For electrical engineers, such as myself, the applications to digital filter design were particularly interesting. There are many other applications described, including the design of lenses, image processing, even neural nets. What I like is that the authors give the backgroud to each application so that even if you are unfamiliar with the field, there are clear and concise introductions that help your understanding of the problem. For example, in the digital filter design section, the authors explain why digital filters are important and how they relate to analog filters. To write a book like this takes not only broad knowledge but extraordinary expository skills.

Fuzzy and Neural Approaches in Engineering (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

Average customer rating:

a great tool for engineering research

Fuzzy and Neural Approaches in Engineering (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Lefteri H. Tsoukalas , and Robert E. Uhrig
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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Fuzzy and Neural Approaches in Engineering, MATLAB Supplement (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

ASIN: 0471160032

Book Description

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. This definitive book presents the fundamentals of both technologies, and demonstrates how to combine the unique capabilities of these two technologies for the greatest advantage. Steering clear of unnecessary mathematics, the book highlights a wide range of dynamic possibilities and offers numerous examples to illuminate key concepts. It also explores the value of relating genetic algorithms and expert systems to fuzzy and neural technologies.

Customer Reviews:

a great tool for engineering research.......1998-04-15

I have been impressed by the clarity of the presented material and the intelligent choice of the various examples that are found in the text. One rarely finds such a well connected presentation of knowledge. This book gave me the capability to further expand my exploration efforts in this very interesting subject.

Introductory Java for Scientists and Engineers

Average customer rating:

Accessible and useful
Begins well, but becomes very complex

Introductory Java for Scientists and Engineers
Richard Davies
Manufacturer: Addison Wesley
ProductGroup: Book
Binding: Textbook Binding

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Java In A Nutshell, 5th Edition

ASIN: 0201398133

Customer Reviews:

Accessible and useful.......2002-07-25

This is a pretty accessible book. It's the first book I ever looked at on Java, and I didn't find it at all difficult to understand the presentation of ideas. About the first half of it is very useful, as well. The second half gets into a graphics library and a numerical functions library, both of which were written for this book. If you have need of such things, you'll find the second half of the book as useful as the first; if not, you'll probably want to move on next to the 4th edition of "Java In A Nutshell," by David Flanagan.

Begins well, but becomes very complex.......2000-03-28

Java is an incredibly popular, albeit arguably difficult language. Many of us in the 'technical trades' may want to learn Java because of its cross-platform or built-in graphics capabilities, but may be put off by many of the Java books intended for computer scientists or web developers. This book begins well, but by chapter 4 is becoming all too complex. I would have liked to have seen far more 'hand-holding' explanation in the middle chapters, and more information couched in terms that may be more familiar to established engineers and scientists, e.g. more examples showing the similarities and differences between Java and older languages such as Fortran, Basic and Pascal.

The Nonlinear Workbook: Chaos, Fractals, Celluar Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov M

Average customer rating:

Good overview of nonlinear dynamics
Helpful in some sections
Useful information, poor presentation

The Nonlinear Workbook: Chaos, Fractals, Celluar Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov M
Willi-Hans Steeb
Manufacturer: World Scientific Publishing Company
ProductGroup: Book
Binding: Paperback

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Chaos and Time-Series Analysis
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering
Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers
Nonlinear Time Series Analysis
An Introduction to Genetic Algorithms (Complex Adaptive Systems)

ASIN: 9812562915

Book Description

The study of nonlinear dynamical systems has advanced tremendously in the last 15 years, making a big impact on science and technology. This book provides all the techniques and methods used in nonlinear dynamics. The concepts and underlying mathematics are discussed in detail.

The numerical and symbolic methods are implemented in C++, SymbolicC++ and Java. Object-oriented techniques are also applied. The book contains more than 100 ready-to-run programs.

The text has also been designed for a one-year course at both the junior and senior levels in nonlinear dynamics. The topics discussed in the book are part of e-learning and distance learning courses conducted by the International School for Scientific Computing.

Customer Reviews:

Good overview of nonlinear dynamics.......2005-11-15

This book is an overview of all of the components of nonlinear dynamics. Nonlinear dynamics is a field of study that enables well-constructed predictive modeling of systems that might be difficult to solve otherwise. Such continuous systems were first widely modeled by ordinary and differential equations, but with the passage of time there are now tools and mathematical models at our disposal that make for a much more concise model of many systems. This workbook tries to touch on all of those mathematical tools. The first six chapters of the book has to do with modeling such complex systems in general, and the rest of the book is a survey of the tools needed to perform complex modeling. The book's format is that of briefly explaining a concept in a few pages, and then presenting a computer program that demonstrates the concept just explained. The explanations are very clear and concise, there are plenty of equations shown, and the accompanying code is well commented. If you want to really drill deeply into any of the concepts then you are going to need some other books. I suggest that for further reading for the mathematically inclined that you pick up "Chaos: An Introduction to Dynamical Systems" by Kathleen Alligood. For scientists that want to see specific problems that can be solved by dynamical systems I suggest the excellent "Nonlinear Dynamics and Chaos: With Applications in Physics, Biology, Chemistry, and Engineering" by Strogatz. The only real complaint I have against this book is that there is uneven coverage of different tools. For example, the author has a great deal to say about neural networks and fuzzy logic, but has very short chapters covering discrete wavelets and cellular automata. More material would have been great, since it is hard to find good books on discrete wavelets and cellular automata in particular. Some readers may also be annoyed that much of the book are code listings of the various demonstration programs. Overall, I would highly recommend it as one of several books that anyone interested in dynamical systems should definitely own. In particular, those individuals interested in the techniques of algorithmic composition of music might find this book a good jumping off point for studying the tools and techniques that make such compositions possible.

Helpful in some sections.......2001-09-01

The topics covered in this book are all important from the standpoint of applications in physics, engineering, computer science, financial engineering, and computational biology. It is written for the person just getting started in these topics, and the author does a fairly good job of discussing them. Readers should not expect, and they will not get, in-depth discussions on these topics, as this would swell the book to 10 times the size. They will however get preparation for moving on to more advanced and complete treatments.

Nonlinear and chaotic maps are considered in chapter 1, with elementary definitions given and six different examples of maps discussed. In discussing the calculation of numerical trajectories of maps, the author deals with the problem of large initial values for the maps and how to implement these in SymbolicC++ and Java. He also shows how to write/read data to a file using C, C++, and JAVA. The exception handling capability of JAVA comes out nicely, but no performance comparison between the three languages for simulating the maps is given by the author. The language REDUCE is used to discuss the stability of the fixed points of the logistic equation, but the code would be useless to the reader who did not have REDUCE since some of the function calls are hidden from the reader. Useful programs are given for calculating the Lyapunov and autocorrelation functions. In addition, C++ programs are given for evaluating the correlation integral for the Henon map. The programs he develops in this chapter can serve as a quick benchmark for one's own programs that calculate the same quantities.

In chapter 2, the author discusses methods for studying time series, including the Lyapunov and Hurst exponents. These two quantities are of enormous importance in the study of dynamical systems, financial data, and network performance. The C++ program that the author gives for calculating the Hurst exponent will not work for arbitrary time intervals. This is followed in the next chapter by a consideration of autonomous systems of ordinary differential equations. The classification of fixed points is considered, and the important concept of a homoclinic orbit. The author gives a nice JAVA program that finds the homoclinic orbit of an anharmonic differential equation using the Lie series technique. The phase portrait of the Van der Pol oscillator is calculated using the Runge-Kutta technique in a C++ program, along with the Lotka-Volterra system from mathematical biology.

Hamiltonian mechanics is discussed in chapter 4, with the important Henon-Heiles model from astrophysics is discussed and JAVA programs given for studying its behavior using the Poincare section technique. Newcomers to this technique will appreciate seeing it done here explicitly. Integrability of Hamiltonian systems using the Lax representation and Floquet theory are also treated, but only at a very rudimentary level. Dissipation is included in the next chapter, and the author discusses the classification of fixed points according to their stability. Lyapunov exponents are again brought into the picture, and the phenomenon of hyperchaos is discussed. Some bifurcation theory is introduced with an example of the Hopf bifurcation. Chapter 6 studies nonlinear driven systems, with the Duffing oscillator treated, and the author gives a useful program for calculating the autocorrelation function of this system. The controlling of chaos with feedback and non-feedback controls is the subject of the next chapter, mostly in the context of difference maps. Fractals finally get introduced in chapter 8, with iterated function systems defined but proofs of their properties omitted. The author gives programs for calculating various popular fractals, such as the dragon, Sierpinski gasket, Koch curve, the Mandelbrot set, and the Julia set. The main disappointment in this chapter is that the author does not give programs for calculating the Hausdorff dimension or capacity, quantities that are notoriously difficult to get a meaningful computational handle on.

The author switches gears in the next chapter and discusses cellular automata, which have recently made a comeback, especially in research on quantum computation. The discussion is too brief however, and does not allow the reader to gain an appreciation of the properties of these important objects. Chapter 10 gives a brief overview of some techniques for solving differential equations, such as the Euler method and the Lie series technique. The latter is not commonly treated in beginning books so its inclusion here is helpful. Symplectic integration is also discussed briefly, but the author does not discuss how to check the integrators using backward integration, which is commonly used in conservative systems modeled by symplectic maps.

Chapter 11, covering neural networks, is the most well-written in the book, and the newcomer to the field will get a fairly decent introduction to the subject. The supplied programs serve to illustrate some of the important concepts in neural networks, such as the Hopfield model, the Kohonen network, the perceptron learning algorithm, and the back-propagation algorithm.

Chapter 12 is an introduction to genetic algorithms, and I find this one particularly nice also, as it does give a rudimentary introduction to what evolutionary algorithms are all about, and gives some elementary genetic programs that find the maximum of one- and two-dimensional maps. He also discusses simulated annealing, and gives a useful program that allows the reader to see clearly how this technique works.

The last chapter covers fuzzy sets and fuzzy logic, which has also taken on importance in recent years, especially in data mining and financial engineering. The programs given to illustrate the concepts are particularly interesting from the standpoint of coding in C++, as the author uses friend functions and operating overloading in some of them. The reader gets a good overview of fuzzy reasoning and fuzzy rule-based systems.

Useful information, poor presentation.......2000-07-19

The information was useful & approprate to the topic. I'd rank it as an average quality refence but a very poor text book.

The text is poorly written. The code is simple and easy to understand, but not very object oriented. There is not enough explanation of the code. The code is not electronically available.

Book Description

This book presents a unified mathematical treatment of diverse problems in mathematics, physics, computer science and engineering using geometric algebra. This text is a practical resource for professionals, researchers, and practitioners, cyberneticists, computer scientists, engineers, applied physicists and applied mathematicians. Several examples are presented to clarify the importance of geometric algebra in signal and image processing, filtering and neural computer, computer vision, robotics and geometric physics. A useful resource to gain a greater understanding of the potential of geometric algebra for the design and implementation of real time artificial systems.

Customer Reviews:

I have not read this book, but can provide this information .......2005-01-25

Part I Advances in Geometric Algebra

Chapter 1
Old Wine in New Bottles: A New Algebraic Framework for Computational Geometry,
David Hestenes
1.1 Introduction
1.2 Minkowski Algebra
1.3 Conformal Split
1.4 Models of Euclidean Space
1.5 Lines and Planes
1.6 Spheres and Hyperplanes
1.7 Conformal and Euclidean Groups
1.8 Screw Mechanics
1.9 Conclusions

Chapter 2
Universal Geometric Algebra
Garret Sobczyk
2.1 Introduction
2.2 The Universal Geometric Algebra
2.3 Matrices of Geometric Numbers
2.4 Linear Transformations
2.5 Pseudo-Euclidean Geometries
2.6 Affine and Projective Geometries
2.7 Conformal Transformations

Chapter 3
Realizations of the Conformal Group
Jose Maria Pozo and Garret Sobczyk
3.1 Introduction
3.2 Projective Geometry
3.3 The Conformal Representant and Stereographic Projection
3.4 Conformal Transformations and Isometries
3.5 Isometries in No
3.6 Compactification
3.7 Mobius Transformations

Chapter 4
Hyperbolic Geometry
Hongbo Li
4.1 Introduction
4.2 Hyperbolic Plane Geometry with Clifford Algebra
4.3 Hyperbolic Conformal Geometry with Clifford Algebra
4.4 A Universal Model for the Conformal Geometries of the Euclidean, Spherica and Double-Hyperbolic Spaces
4.5 Conclusion

Part II Theorem Proving

Chapter 5
Geometric Reasoning With Geometric Algebra
Dongming Wang
5.1 Introduction
5.2 Clifford Algebra for Euclidean Geometry
5.4 Proving Identities in Clifford Algebra

Chapter 6
Automated Theorem Proving
Hongbo Li
6.1 Introduction
6.2 A general Framework for Clifford algebra and Wu's Method
6.3 Automated Theorem Proving in Euclidean Geometry and Other
Classical Geometries
6.4 Automated Theorem Proving in Differential Geometry}{116}
6.5 Conclusion

Part III Computer Vision

Chapter 7
The Geometry Algebra of Computer Vision
Eduardo Bayro Corrochano and Joan Lasenby
7.1 Introduction
7.2 The Geometric Algebras of 3-D and 4-D Spaces
7.3 The Algebra of Incidence
7.4 Algebra in Projective Space
7.5 Visual Geometry of $n$ Uncalibrated Camera
7.6 Conclusions

Chapter 8
Using Geometric Algebra for Optical Motion Capture
Joan Lasenby and Adam Stevenson
8.1 Introduction
8.2 External and Internal Calibration
8.3 Estimating the External Parameters
8.4 Examples and Results
8.5 Extending to Include Internal Calibration
8.6 Conclusions

Chapter 9
Bayesian Inference and Geometric Algebra: An Application to Camera Localization
Chris Doran
9.1 Introduction
9.2 Geometric Algebra in Three Dimensions
9.3 Rotors and Rotations
9.4 Rotor Calculus
9.5 Computer Vision
9.6 Unknown range data
9.7 Extension to three cameras
9.8 Conclusions

Chapter 10
Projective Reconstruction of Shape and Motion Using Invariant Theory
Eduardo Bayro Corrochano and Vladimir Banarer
10.1 Introduction
10.2 3-D Projective Invariants from Multiple Views
10.3 Projective Depth
10.4 Shape and Motion
10.5 Conclusions

Part IV Robotics

Chapter 11
Robot Kinematics and Flags

11.1 Introduction
11.2 The Clifford Algebra
11.3 Flags
11.4 Robots
11.5 Concluding Remarks

Chapter 12
The Clifford Algebra and the Optimization of Robot Design
Shawn G. Ahlers and John Michael McCarthy
12.1 Introduction
12.2 Literature Review
12.3 Overview of the Design Algorithm
12.4 Double Quaternions
12.5 The Task Trajectory
12.6 The Design of the TS Robot
12.7 The Optimum TS Robot
12.8 Conclusion

Chapter 13
Eduardo Bayro Corrochano and Garret Sobczyk
13.1 Introduction
13.2 The General Linear Group
13.3 Algebra of Incidence
13.4 Rigid Motion in the Affine Plane
13.5 Application to Robotics
13.6 Application II:
The design of an image filter
Recognition of hand gestures
The meet filter
13.7 Conclusion

Part V Quantum and Neural Computing, and Wavelets

Chapter 14
Geometric Algebra in Quantum Information Processing
by Nuclear Magnetic Resonance
Timothy F. Havel, David G. Cory, Shyamal S. Somaroo, and Ching-Hua Tseng
14.1 Introduction
14.2 Multiparticle Geometric Algebra
14.3 Algorithms for Quantum Computers
14.4 NMR and the Product Operator Formalism
14.5 Quantum Computing by Liquid-State NMR
14.6 States and Gates by NMR
14.7 Quantum Simulation by NMR
14.8 Remarks on Foundational Issues

Chapter 15
Geometric Feedforward Neural Networks and Support Multivector Machines
Eduardo Bayro Corrochano and Refugio Vallejo

15.1 Introduction
15.2 Real Valued Neural Networks
15.3 Complex MLP and Quaternionic MLP
15.4 Geometric Algebra Neural Networks
15.5 Learning Rule
15.6 Experiments Using Geometric Feedforward Neural Networks
15.7 Support Vector Machines in Geometric Algebra
15.8 Experimental Analysis of Support Multivector Machines
15.9 Conclusions

Chapter 16
Image Analysis Using Quaternion Wavelets
Leonardo Traversoni
16.1 Introduction
16.2 The Static Approach
16.3 Clifford Multiresolution Analyses
16.4 Haar Quaternionic Wavelets
16.5 A Dynamic Interpretation
16.6 Global Interpolation
16.7 Dealing with Trajectories
16.8 Conclusions

Part VI Applications to Engineering and Physics

Chapte 17
Objects in Contact: Boundary Collisions as Geometric Wave Propagation
Leo Dorst
17.1 Introduction
17.2 Boundary Geometry
17.3 The Boundary as a Geometric Object
17.4 Wave Propagation of Boundaries
17.5 Conclusions

Chapter 18 Modern Geometric Calculations in Crystallography
G. Aragon, J.L. Aragon, F. Davila, A. Gomez and M.A. Rodriguez
18.1 Introduction
18.2 Quasicrystals
18.3 The Morphology of Icosahedral Quasicrystals
18.4 Coincidence Site Lattice Theory
18.5 Conclusions

Chapter 19 Quaternion Optimization Problems in Engineering
Ljudmila Meister
19.1 Introduction
19.2 Properties of Quaternions
19.3 Extremal Problems for Quaternions
19.4 Determination of Rotations
19.5 The Main Problem of Orientation
19.6 Optimal Filtering and Prediction
19.7 Summary

Chapter 20
Clifford Algebras in Electrical Engineering
William Baylis
20.1 Introduction
20.2 Structure of Cl_3
20.3 Paravector Model of Spacetime
20.4 Using Relativity at Low Speeds
20.5 Relativity at High Speeds
20.6 Conclusions

Chapter 21
Applications of Geometric Algebra in Physics and Links With Engineering
Anthony Lasenby and Joan Lasenby

21.1 Introduction
21.2 The Spacetime Algebra
21.3 Quantum Mechanics
21.4 Gravity as a Gauge Theory
21.5 A New Representation of 6-d Conformal Space
21.6 Summary and Conclusions

Part VII Computational Methods in Clifford Algebras

Chapter 22
Clifford Algebras as Projections of Group Algebras
Vladimir M. Chernov
22.1 Introduction
22.2 Group Algebras and Their Projection
22.3 Applications
22.4 Conclusion

Chapter 23
Counterexamples for Validation and Discovering of New Theorems
Pertti Lounesto
23.1 Introduction
23.2 The Role of Counterexamples in Mathematics
23.3 Clifford Algebras: An Outline
23.4 Preliminary Counterexamples in Clifford Algebras
23.5 Counterexamples About Spin Groups
23.6 Counterexamples on the Internet

Chapter 24
The Making of GABLE: A Geometric Algebra Learning Environment in Matlab
Stephen Mann, Leo Dorst, and Tim Bouma
24.1 Introduction
24.2 Representation of Geometric Algebra
24.3 Inverses
24.4 Meet and Join
24.5 Graphics
24.6 Example: Pappus's Theorem
24.7 Conclusions

Chapter 25
Helmstetter Formula and Rigid Motions with CLIFFORD
Rafal Ablamowicz
25.1 Introduction
25.2 Verification of the Helmstetter Formula
25.3 Rigid Motions with Clifford Algebras
25.4 Summary

References

Index

Average customer rating:

Exact same content as the cheaper and sturdy paperback version
explains many key ideas

The Nonlinear Workbook: Chaos, Fractals, Celluar Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hiddn Markov Mo
Willi-Hans Steeb
Manufacturer: World Scientific Publishing Company
ProductGroup: Book
Binding: Hardcover

General | Certification Central | Computers & Internet | Subjects | Books

General | Mathematics | Science | Subjects | Books

General & Reference | Technology | Science | Subjects | Books

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All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 9812562788

Book Description

The study of nonlinear dynamical systems has advanced tremendously in the last 20 years, making a big impact on science and technology. This book provides all the techniques and methods used in nonlinear dynamics. The concepts and underlying mathematics are discussed in detail.

The numerical and symbolic methods are implemented in C++, SymbolicC++ and Java. Object-oriented techniques are also applied. The book contains more than 150 ready-to-run programs.

Customer Reviews:

Exact same content as the cheaper and sturdy paperback version.......2007-07-20

Overall, this is a good book on the various subjects it covers, but I have to wonder - why would you buy this expensive hardcover version when the paperback version costs a little more than half what this hardcover costs? The contents are the same, the publication date is the same, and having had my softcover copy for two years and made heavy use of it, it seems to be standing up to the wear and tear just fine.

This book is an overview of all of the components of nonlinear dynamics. Nonlinear dynamics is a field of study that enables well-constructed predictive modeling of systems that might be difficult to solve otherwise. Such continuous systems were first widely modeled by ordinary and differential equations, but with the passage of time there are now tools and mathematical models at our disposal that make for a much more concise model of many systems. This workbook tries to touch on all of those mathematical tools.

The first six chapters of the book has to do with modeling such complex systems in general, and the rest of the book is a survey of the tools needed to perform complex modeling. The book's format is that of briefly explaining a concept in a few pages, and then presenting a computer program that demonstrates the concept just explained. The explanations are very clear and concise, there are plenty of equations shown, and the accompanying code is well commented. If you want to really drill deeply into any of the concepts then you are going to need some other books. I suggest that for further reading for the mathematically inclined that you pick up "Chaos: An Introduction to Dynamical Systems" by Kathleen Alligood. For scientists that want to see specific problems that can be solved by dynamical systems I suggest the excellent "Nonlinear Dynamics and Chaos: With Applications in Physics, Biology, Chemistry, and Engineering" by Strogatz. The only real complaint I have against this book is that there is uneven coverage of different tools. For example, the author has a great deal to say about neural networks and fuzzy logic, but has very short chapters covering discrete wavelets and cellular automata. More material would have been great, since it is hard to find good books on discrete wavelets and cellular automata in particular. Some readers may also be annoyed that much of the book are code listings of the various demonstration programs.

Overall, I would highly recommend this as one of several books that anyone interested in dynamical systems should definitely own. In particular, those individuals interested in the techniques of algorithmic composition of music might find this book a good jumping off point for studying the tools and techniques that make such compositions possible.

explains many key ideas.......2005-12-08

Here is a text of advanced nonlinear dynamics. Geared towards the intensive use of computers to perform the necessary grungework. Steeb hits on many important ideas that have emerged in recent decades. He shows the interrelation between chaotic phenomena and fractals, and how fractals can be used to describe the onset to chaos.

The Hidden Markov Models have proved to be the key idea in current Automatic Speech Recognisers. A tribute to the practical nature of this idea.

Steeb's discussion of neural networks and genetic algorithms is enough to get you started in this field. Ideas like forward and back propagation for feedback are clearly explained.

The sample code should be welcomed. It lets you see for yourself on your own computer, and to tinker with the various parameters. Though I am unsure about the choice of code in Symbolic C++. Unlike C++ or Java, this is a rarely used language.

Artificial Neural Networks for Civil Engineers: Fundamentals and Applications

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Artificial Neural Networks for Civil Engineers: Fundamentals and Applications

Manufacturer: American Society of Civil Engineers
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ASIN: 0784402256

Compartmental Modeling with Networks (Modeling and Simulation in Science, Engineering and Technology)

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Compartmental Modeling with Networks (Modeling and Simulation in Science, Engineering and Technology)
Gilbert G. Walter , and Martha Contreras
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ASIN: 0817640193

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This new advanced text/reference book presents compartmental models or flow models from an applications perspective. Flow models are used when there is a flow of information or materials, such as fluid of money or energy between the components of a system. They are widely used in biomedicine but have also useful applications in studying ecosystems, input-output analysis, competition, models, and epidemology. In order to provide a complete and balanced presentation, the book is organized in four parts. Part 1 is devoted to the theory of digraphs; Part 2 addresses Markov chains; Part 3 discusses differential equations; and Part 4 presents the theory of compartmental models, addressing the relations between dynamics of the solution and structure of the model. Key applications discussed include ecosystem models, fluid transfer, competition models, tracer kinetic experiments, and network flows. Essential topics and methods are presented in an accessible style with many examples: directed graphs, differential equations, Markov chains, and compartmental model construction. The material provides a thorough and comprehensive presentation of compartmental models, model construction and applications. All advanced students, practitioners and professionals in bioscience and applied mathematical modeling will find the book a useful and excellent reference.

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