Neural Network Parallel Computing

Erscheinungsjahr 2012
ISBN: 978-1-4613-6620-1
Verlag: Springer

Buch, Englisch, 230 Seiten, Format (B × H): 160 mm x 240 mm, Gewicht: 403 g

Reihe: The Springer International Series in Engineering and Computer Science

is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications.
presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as -queen, crossbar switch scheduling, four-coloring and -colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling.
is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.

Zielgruppe

Research

Autoren/Hrsg.

Takefuji, Yoshiyasu

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise
• Mathematik | Informatik EDV | Informatik Informatik
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

1. Neural Network Models and N-Queen Problems.- 2. Crossbar Switch Scheduling Problems.- 3. Four-Coloring and K-Colorability Problems.- 4. Graph Planarization Problems.- 5. Channel Routing Problems.- 6. RNA Secondary Structure Prediction.- 7. Knight’s Tour Problems.- 8. Spare Allocation Problems.- 9. Sorting and Searching.- 10. Tiling Problems.- 11. Silicon Neural Networks.- 12. Mathematical Background.- 13. Forthcoming Applications.- 14. Conjunctoids and Artificial Learning.