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Building Neural Networksby David M. Skapura
Synopses & Reviews
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:
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.
Book News Annotation:
A practical introduction to the problems neural network technology can solve. Intended for upper-level undergraduate or first-year graduate students in engineering or computer science courses or for industry engineers who wish to explore practical aspects of neural networking. Requires a good understanding of vector and matrix mathematics.
Annotation c. Book News, Inc., Portland, OR (booknews.com)
About the Author
About David M. Skapura
Table of Contents
Single Neuron Computations.
The Backpropagation Network.
The Counterpropagation Network.
Adaptive Resonance Theory.
The Multidirectional Associative Memory.
The Hopfield Memory.
Developing a Data Representation.
Pattern Representation Methods.
Training and Performance Evaluation.
A Practical Example.
Business and Financial Applications.
Predicting Commodity Futures.
Gender Recognition from Facial Images.
Imagery Feature Discovery.
Aircraft Tracking in Video Imagery.
Process Control and Robotics.
Robotic Manipulator Control.
Fuzzy Neural Systems.
Implementation of a Fuzzy Network.
Fuzzy Neural Inference.
Fuzzy Control of BPN Learning.
Fuzzy Neural-System Summary.
Answers to Selected Exercises.
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