Description: 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 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 to 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; business and financial systems; data extraction from images; mechanical process control systems; and new neural networks that combinepattern 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.

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