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.
There has been a resurgence of interest in artificial neural networks over the last
few years, as researchers from diverse backgrounds have produced a firm theoretical
foundation and demonstrated numerous applications of this rich field of
study. However, the interdisciplinary nature of neural networks complicates the
development of a comprehensive, but introductory, treatise on the subject. Neural
networks are useful tools for solving many types of problems. These problems
may be characterized as mapping (including pattern association and pattern classification),
clustering, and constrained optimization. There are several neural networks
available for each type of problem. In order to use these tools effectively
it is important to understand the characteristics (strengths and limitations) of each.
This book presents a wide variety of standard neural networks, with diagrams
of the architecture, detailed statements of the training algorithm, and several
examples of the application for each net. In keeping with our intent to show
neural networks in a fair but objective light, typical results of simple experiments
are included (rather than the best possible). The emphasis is on computational
characteristics, rather than psychological interpretations. TO illustrate the similarities
and differences among the neural networks discussed, similar examples
are used wherever it is appropriate.
Fundamentals of Neural Networks has been written for students and for
researchers in academia, industry, and govemment who are interested in using
neural networks. It has been developed both as a textbook for a one semester,
or two quarter, Introduction to Neural Networks course at Florida Institute of
Technology, and as a resource book for researchers. Our course has been developed
jointly by neural networks researchers from applied mathematics, com
puter science, and computer and electrical engineering. Our students are seniors,
or graduate students, in science and engineering; many work in local industry.