Building Neural Networks [Paperback]

David M. Skapura (Author)

Book Description

Publication Date: December 1, 1995 | ISBN-10: 0201539217 | ISBN-13: 978-0201539219 | Edition: 1

This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author explains the process of building neural-network applications that utilize recent connectionist developments, and conveys an understanding both of the potential, and the limitations of different network models. Examples are described in enough detail for you to assimilate the information and then use the accumulated experience of others to create your own applications. These examples are deliberately restricted to those that can be easily understood, and recreated, by any reader, even the novice practitioner. In some cases the author describes alternative approaches to the same application, to allow you to compare and contrast their advantages and disadvantages. *Organized by application areas, rather than by specific network architectures or learning algorithms, Building Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems.Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models. Finally, the book provides information on the practical aspects of application design, and contains six topic-oriented chapters on specific applications of neural-network systems. These applications include networks that perform: **-Pattern matching, storage, and recall.-Business and financial systems.-Data extraction from images.-Mechanical process control systems.-New neural networks that combine pattern matching with fuzzy logic. The book includes application-oriented exercises that further help you see how a neural network solves a problem, and that reinforce your understanding of modeling techniques.

Editorial Reviews

From the Back Cover

This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author explains the process of building neural-network applications that utilize recent connectionist developments, and conveys an understanding both of the potential, and the limitations of different network models. Examples are described in enough detail for you to assimilate the information and then use the accumulated experience of others to create your own applications. These examples are deliberately restricted to those that can be easily understood, and recreated, by any reader, even the novice practitioner. In some cases the author describes alternative approaches to the same application, to allow you to compare and contrast their advantages and disadvantages.

Organized by application areas, rather than by specific network architectures or learning algorithms, Builiding Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models. Finally, the book provides information on the practical aspects of application design, and contains six topic-oriented chapters on specific applications of neural-network systems. These applications include networks that perform:

• Pattern matching, storage, and recall
• Business and financial systems
• Data extraction from images
• Mechanical process control systems
• New neural networks that combine pattern matching with fuzzy logic

The book includes application-oriented excercises that further help you see how a neural network solves a problem, and that reinforce your understanding of modeling techniques.

0201539217B04062001

About the Author

About David M. Skapura

David M. Skapura is the coauthor, with James A. Freeman, of Neural Networks: Algorithms, Applications, and Programming Techniques. He is currently employed by Brightware Corporation (a spin-off of Inference Corporation), where he works as an applications consultant, developing customized knowledge-based systems and applications. He is also the founder and president of Scient Computing, a small Houston consulting firm specializing in neural-networking applications and research. Previously at Loral Space Information Systems, Skapura investigated the applicability of neural networks to NASA's advanced automation requirements. He is an adjunct professor at the University of Houston at Clear Lake.

0201539217AB04062001

Product Details

• Paperback: 304 pages
• Publisher: Addison-Wesley Professional; 1 edition (December 1, 1995)
• Language: English
• ISBN-10: 0201539217
• ISBN-13: 978-0201539219
• Product Dimensions: 9.5 x 6.6 x 0.8 inches
• Shipping Weight: 1 pounds (View shipping rates and policies)
• Average Customer Review: 2.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,574,697 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

30 of 31 people found the following review helpful:

2.0 out of 5 stars okay starting point, be prepared to buy a more thorough text, October 4, 1999

By A Customer

This review is from: Building Neural Networks (Paperback)

The book serves as a luke warm introdution to neural networks. For the reader planning on applying the material in an industrial setting the book is far from sufficient. An average entry-level programmer could probably successfully code a couple of different types of neural networks as the book supplies nicely written pseudo-code for only couple types. As soon as the reader is interested in pursuing any kind of variation on these basic networks he hits a dead-end wall with 'references for further study' carved in the concrete.

For early undergraduate and advanced highschool students the text provides a great introduction to the field without wasting time on opinion and praising. Rather the reader can dive write into the heart of basic neural network algorithms and brief analyses of why they work and what they are good for.