Cellular Nonlinear/Neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Leon Chua, co-inventor of the CNN, and Tamàs Roska are both highly respected pioneers in the field.
Record details
ISBN:0521652472
Physical Description:print xi, 396 p. : ill. ; 26 cm.
Publisher:Cambridge, U.K. ; New York, NY : Cambridge University Press, 2002.
Content descriptions
Bibliography, etc. Note:
Includes bibliographical references (p. 339-360) and index.
Formatted Contents Note:
Introduction -- 2. Notation, definitions, and mathematical foundation -- 3. Characteristics and analysis of simple CNN templates -- 4. Simulation of the CNN dynamics -- 5. Binary CNN characterization via Boolean functions -- 6. Uncoupled CNNs: unified theory and applications -- 7. Introduction to the CNN Universal Machine -- 8. Back to basics: Nonlinear dynamics and complete stability -- 9. The CNN Universal Machine (CNN-UM) -- 10. Template design tools -- 11. CNNs for linear image processing -- 12. Coupled CNN with linear synaptic weights -- 13. Uncoupled standard CNNs with nonlinear synaptic weights -- 14. Standard CNNs with delayed synaptic weights and motion analysis
Book
