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Product Description
Neural Networks, Second Edition provides a complete introduction to neural networks. It describes what they are, what they can do, and how they do it. While some scientific background is assumed, the reader is not expected to have any prior knowledge of neural networks. These networks are explained and discussed by means of examples, so that by the end of the book the reader will have a good overall knowledge of developments right up to current work in the field.
· Updated and expanded second edition
· Main networks covered are: feedforward networks such as the multilayered perceptron, Boolean networks such as the WISARD, feedback networks such as the Hopfield network, statistical networks such as the Boltzmann machine and Radial-Basis function networks, and self-organising networks such as Kohonen's self-organizing maps. Other networks are referred to throughout the text to give historical interest and alternative architectures
· The applications discussed will appeal to student engineers and computer scientists interested in character recognition, intelligent control and threshold logic. The final chapter looks at ways of implementing a neural network, including electronic and optical systems
This book is suitable for undergraduates from Computer Science and Electrical Engineering Courses who are taking a one module course on neural networks, and for researchers and computer science professionals who need a quick introduction to the subject.
PHIL PICTON is Professor of Intelligent Computer Systems at University College Northampton. Prior to this he was a lecturer at the Open University where he contributed to distance learning courses on control engineering, electronics, mechatronics and artificial intelligence. His research interests include pattern recognition, intelligent control and logic design.
· Updated and expanded second edition
· Main networks covered are: feedforward networks such as the multilayered perceptron, Boolean networks such as the WISARD, feedback networks such as the Hopfield network, statistical networks such as the Boltzmann machine and Radial-Basis function networks, and self-organising networks such as Kohonen's self-organizing maps. Other networks are referred to throughout the text to give historical interest and alternative architectures
· The applications discussed will appeal to student engineers and computer scientists interested in character recognition, intelligent control and threshold logic. The final chapter looks at ways of implementing a neural network, including electronic and optical systems
This book is suitable for undergraduates from Computer Science and Electrical Engineering Courses who are taking a one module course on neural networks, and for researchers and computer science professionals who need a quick introduction to the subject.
PHIL PICTON is Professor of Intelligent Computer Systems at University College Northampton. Prior to this he was a lecturer at the Open University where he contributed to distance learning courses on control engineering, electronics, mechatronics and artificial intelligence. His research interests include pattern recognition, intelligent control and logic design.
About the Author
PHIL PICTON is a Reader in Engineering Control Systems at Nene College in Northampton. Prior to this he was a lecturer at the Open University where he contributed to distance learning courses on control engineering, electronics, mechatronics and artificial intelligence. His research interests include patten recognition, intelligent control and logic design.
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Most Helpful Customer Reviews
7 of 7 people found the following review helpful
Format:Hardcover
Currently many people know how to 'use' Neural Networks, but very rare who know deeply what is going on behind. This is because they overlook the basic concepts at their first learning of a certain Neural Network. On the other hand, there are still not many books with the intention to build a firm understanding of the fundamental concepts. Instead of 'begin' with the basic, many books eagerly rammed down your brain with all the 'hoopla' of fast proliferation in Neural Networks community and leave you in preplexity (If you read Haykin's Comprehensive Foundations as your first reading aims at understand the basic, you will soon know what I mean). In such condition, how could you even be able to develop your own simple Neural Networks ?
Before you start get in deep with any kinds of Neural Networks widely used today (backpropagation, rbf, Hopfield, Boltzman Machine, probabilistic neural net, etc) you can try this book at first. After knowing the basic mechanism, you could savely learn from some advance books (or papers), and so far I think 'Introduction to Neural Computations by John Hertz et.al.' is still the best though a bit outdate.
This book give me a lucid presentation about the basic 'mechanism' behind Neural Networks.
After I read this book, I could only give it five stars :-).
6 of 6 people found the following review helpful
Format:Paperback
Completing a project-based course unit at the end of my degree, I needed a little clarification and elaboration on some topics. A word with my tutor led to the purchase of this book.
I cannot thank him enough! This book is cutting-edge material for final-year graduate and post-graduate study; it's also presented in a way which does not require the reader to have a degree in order to understand it.
I take my hat off to Professor Picton; this is a serious academic text which is also accessible to the newcomer; the kind of example which should be adopted by more academic authors. Excellent.
2 of 2 people found the following review helpful
Format:Paperback
This is the true "Idiot's guide" to Neural networks, very good introduction, examples that keep one interested and a lot of pointers to further reading.
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