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This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
- Sales Rank: #44127 in Books
- Published on: 2014-09-01
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.81" w x 7.50" l, 2.98 pounds
- Binding: Paperback
- 800 pages
About the Author
Martin T. Hagan (Ph.D. Electrical Engineering, University of Kansas) has taught and conducted research in the areas of control systems and signal processing for the last 35 years. For the last 25 years his research has focused on the use of neural networks for control, filtering and prediction. He is a Professor in the School of Electrical and Computer Engineering at Oklahoma State University and a co-author of the Neural Network Toolbox for MATLAB. Howard B. Demuth (Ph.D. Electrical Engineering, Stanford University) has twenty-three years of industrial experience, primarily at Los Alamos National Laboratory, where he helped design and build one of the world's first electronic computers, the "MANIAC." Demuth has fifteen years teaching experience as well. He is co-author of the Neural Network Toolbox for MATLAB and currently teaches a Neural Network course for the University of Colorado at Boulder. Mark Hudson Beale (B.S. Computer Engineering, University of Idaho) is a software engineer with a focus on artificial intelligence algorithms and software development technology. Mark is co-author of the Neural Network Toolbox for MATLAB and provides related consulting through his company, MHB Inc., located in Hayden, Idaho. Orlando De Jes�s (Ph.D. Electrical Engineering, Oklahoma State University) has twenty-four years of industrial experience, with AETI C.A. in Caracas, Venezuela, Halliburton in Carrollton, Texas and is currently working as Engineering Consultant in Frisco, Texas. Orlando’s dissertation was a basis for the dynamic network training algorithms in the Neural Network Toolbox for MATLAB.
Most helpful customer reviews
46 of 47 people found the following review helpful.
Excellent intro to NN maths but few practical advices
By Hans Ivers (hans.ivers@psy.ulaval.ca)
I read the entire book over a one-semester graduate course in NN. I was amazed by the quality of formalism (notation), which allow me to understand quite easily complex mathematical concepts, algorithms and proofs presented throughout the book. Authors introduced in an effective way all important mathematical concepts before using them. I felt this book is accessible for a beginner in NN field but you will need a good basis (one or more undergraduate courses) in linear algebra and calculus. Overall, this book constitutes an excellent introduction to NN but you will need an additional book to help you through more practical aspects of NN training. My suggestions are Chris Bishop (1995) Neural Networks for Pattern Recognition (chap. 8-9). or Reed & al. (1999). Neural Smithing : Supervised Learning in Feedforward Artificial Neural Networks.
16 of 16 people found the following review helpful.
Excellent book for understanding neural network innards.
By A Customer
I took a graduate neural networks course with Dr. Hagan who used this book. The book analyzes the contemporary algorithms for neural nets and shows why neural nets work (and don't work). MATLAB examples are on the supplemental disk but they can be coded easily in other languages. The convergence toward a solution is shown using 2D and 3D plots.
17 of 18 people found the following review helpful.
Hands down the best introduction
By Louis Charbonneau
I knew the very poor Matlab Neural Network Toolbox User's Guide by the same authors and I was kind of expecting the same, and boy was I wrong!
This book is simply brilliant, a miracle of pedagogy. It is intended for undergrad classes, but it is so clear that graduate students will benefit enormously from reading it before any other material. Plainly put, this book makes you UNDERSTAND this difficult topic, more than any other book that I know of (Zurada, Smith, Hassoun, Haykin, Duda-Hart, Caudill, etc)
A selection of worked out problems are included at the end of each chapter, a practice that is highly beneficial but alas too rare in books of the kind.
I very much appreciated the very clear exposition of backpropagation, and optimization methods such as Levenberg-Marquardt.
A note to Matlab users: funky demos are available for free and illustrate the main points of the book.
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