Freeman, James A. <br/><br/>
Simulating neural networks with Mathematica /  
James A. Freeman. 
 - Reading, Mass. :    Addison-Wesley,    c1994. 
 - x, 341 p. :    ill. ;    25 cm. 
<br/><br/>Includes bibliographical references (p. 335-336) and index. 
<br/><br/>Introduction to Neural Networks and Mathematica. --  Training by Error Minimization. --  Backpropagation and Its Variants. --  Probability and Neural Networks. --  Optimization and Constraint Satisfaction with Neural Networks. --  Feedback and Recurrent Networks. --  Adaptive Resonance Theory. --  Genetic Algorithms. 
<br/><br/> This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. Also for researchers and practitioners interested in using Mathematica as a research tool.Features 
<br/><br/><label>ISBN: </label>020156629X 9780201566291 
<br/><br/><label>LCCN: </label>92002345 
<br/><br/><label>Subjects--Uniform Titles: </label><br/>CIT.
<br/><br/><label>Subjects--Topical Terms: </label><br/>Neural networks (Computer science)
<br/><br/><label>LC Class. No.: </label>QA76.87   /  .F72 1994 
<br/><br/><label>Dewey Class. No.: </label>006.3