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<CENTER><B><FONT size=+2>Neural Network Design</FONT></B></CENTER>
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<CENTER><B>Martin T. Hagan, Howard B. Demuth, Mark H. Beale</B></CENTER>
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<CENTER><B>ISBN: </B>0-9717321-0-8</CENTER>
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<P>NEURAL NETWORK DESIGN provides a clear and detailed survey of fundamental
neural network architectures and learning rules. In it, the authors emphasize
mathematical analysis of networks, methods for training networks, and
application of networks to practical engineering problems in pattern
recognition, signal processing, and control systems. The book incorporates
necessary background material (such as linear algebra, optimization, and
stability) to the extent it is needed. The book includes extensive coverage of
performance learning, including the Widrow-Hoff rule and backpropagation. The
authors describe several enhancements of backpropagation, such as the conjugate
gradient and Levenberg-Marquardt variations. These techniques are illustrated
with applications in pattern recognition, adaptive filtering, and function
approximation. The authors use simple building blocks to explain associative and
competitive networks, including feature maps, learning vector quantization, and
adaptive resonance theory. Recurrent associative memory networks, such as the
Hopfield network, are also discussed. All topics are systematically presented in
a unified framework with a consistent notation. Detailed examples and numerous
solved problems are included.</P>
<P>Optional exercises incorporating the use of MATLAB are built into each
chapter, and a set of <A href="http://hagan.okstate.edu/nndesign_5.ZIP">Neural
Network Design Demonstrations</A> make use of MATLAB to illustrate important
concepts. In addition, the book's straightforward organization -- with each
chapter divided into the following sections: Objectives, Theory and Examples,
Summary of Results, Solved Problems, Epilogue, Further Reading, and Exercises --
makes it an excellent tool for learning and continued reference.</P>
<H3>Ordering Information</H3>
<P>This book can be obtained from the University of Colorado Bookstore. Contact
John Stovall at 303-492-3648, or by email at john.stovall@colorado.edu.</P>
<H3>Sample Chapters (PDF)</H3>
<UL>
<LI><A href="http://hagan.okstate.edu/NNTOC.pdf">Table of Contents</A>
<LI><A href="http://hagan.okstate.edu/1_Intro.pdf">Chapter 1 Introduction</A>
<LI><A href="http://hagan.okstate.edu/2_Architectures.pdf">Chapter 2 Neuron
Model and Network Architectures</A>
<LI><A href="http://hagan.okstate.edu/3_IllustrEx.pdf">Chapter 3 An
Illustrative Example</A>
<LI><A href="http://hagan.okstate.edu/4_Perceptron.pdf">Chapter 4 Perceptron
Learning Rule</A> </LI></UL>
<H3>Related Resources</H3>
<P>Transparency Masters (You will need <A
href="http://www.stuffit.com/win/standard/trial.html">Stuffit Expander</A> for
the PDF Files)</P>
<UL>
<LI><A href="http://hagan.okstate.edu/transmastrs.zip">Transparency Masters
(3.6 MB) (Powerpoint)</A>
<LI><A href="http://hagan.okstate.edu/PDF_1.sit">Transparency Masters Ch1-Ch11
(PDF)</A>
<LI><A href="http://hagan.okstate.edu/PDF_2.sit">Transparency Masters
Ch12-Ch18 (PDF)</A> </LI></UL>
<P>Video Lectures</P>
<UL>
<LI>Video lectures for a 15 week course covering most of the textbook are
available from the Oklahoma State University College of Engineering,
Architecture and Technology Extension Office. Contact Laura Gann, Manager,
Engineering Extension, (405)<FONT face="Times New Roman"
size=3>744-9227</FONT><SPAN class=EUDORAHEADER>.</SPAN> </LI></UL>
<P>Neural Network Design Demonstrations</P>
<UL>
<LI><A href="http://hagan.okstate.edu/nndesign_5.ZIP">Demo MATLAB files</A>
<LI><A href="http://hagan.okstate.edu/mininnet.zip">MININNET</A></LI></UL>
<P>Instructor's Manual</P>
<UL>
<LI>To obtain a copy of the INSTRUCTOR'S MANUAL contact the University of
<BR>Colorado Bookstore, phone 303-492-3648. Ask specifically for an
<BR>instructor's manual if you are instructing a class and want
one.<BR></LI></UL></BODY></HTML>
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