Fundamentals of Neural Networks: Architectures, Algorithms And Applications [Paperback]

Laurene V. Fausett (Author)

Book Description

ISBN-10: 0133341860 | ISBN-13: 978-0133341867 | Publication Date: December 19, 1993 | Edition: 1

Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.

Editorial Reviews

From the Publisher

An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understudy with many examples.

From the Back Cover

Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.

Product Details

• Paperback: 461 pages
• Publisher: Prentice Hall; 1 edition (December 19, 1993)
• Language: English
• ISBN-10: 0133341860
• ISBN-13: 978-0133341867
• Product Dimensions: 9.1 x 7 x 1 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars  See all reviews (12 customer reviews)
• Amazon Best Sellers Rank: #253,965 in Books (See Top 100 in Books)
  • #22 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Neural Networks
  • #23 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Computer Mathematics

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Customer Reviews

Most Helpful Customer Reviews

22 of 22 people found the following review helpful:

5.0 out of 5 stars Solid and Complete, December 14, 1999

By 

Larry M. Deschaine, PE - See all my reviews

This review is from: Fundamentals of Neural Networks: Architectures, Algorithms And Applications (Paperback)

This text explains the why and how for understanding neural networks, beginning with thier biological counterparts, where NN are used, why, and how. Detailed discussions on the Hebb, Perceptron, and Adaline pattern classification nets are provided, as well as fixed wieght competitive nets, Kohonen Self-Organizing Maps, Learning Vector Quantification, Counter Propagation, and Back Propagation, just to name a few. I received the specific theoretical foundation for neural net deployment on projects with confidence, and have referenced the work in breakthrough research on machine learning.

Highly recommended for the serious researcher or scientist/engineer/analyst deploying neural networks.

26 of 28 people found the following review helpful:

5.0 out of 5 stars Clear and Well Organized, June 27, 2000

By 

Nick Arling - See all my reviews

This review is from: Fundamentals of Neural Networks: Architectures, Algorithms And Applications (Paperback)

I'm a senior in a Mechanical Engineering undergraduate program, and am researching ANN's for a professor. I had almost no knowlege of ANN's, and had tried finding a good overview of the subject as well as a clear description of algorithms used in ANN's. After looking at three other books, I was relieved to find this one. Also, it's organizational structure is the most sensible I've seen.

9 of 9 people found the following review helpful:

3.0 out of 5 stars Good, but not very mathematical, January 16, 2006

By 

Sidhant - See all my reviews

This review is from: Fundamentals of Neural Networks: Architectures, Algorithms And Applications (Paperback)

This is an excellent textbook for beginners, giving a clear picture of what neural networks are, and where they are used. It also talks about back-propagation, associative neural nets, and more. But the biggest flaw is that the book has little mathematics. And it also doesnt have any working code (only pseudo-code). So if you are considering buying this as a textbook for a NN course you are taking at your university, well, I would suggest you take a good look at the book at your library before you decide to buy it. Most university courses put neural nets on a firm mathematical footing and might also have course projects that have to be done by the student. This book can help you with neither of these. And the book's pretty expensive, I really wonder why.