Neural Networks [E-Book] : An Introduction / by Berndt Müller, Joachim Reinhardt, Michael T. Strickland.
Müller, Berndt, (author)
Reinhardt, Joachim, (author) / Strickland, Michael T., (author)
Berlin, Heidelberg : Springer, 1995
XV, 331 p. online resource.
englisch
9783642577604
10.1007/978-3-642-57760-4
Physics of Neural Networks
Full Text
Table of Contents:
  • 1. The Structure of the Central Nervous System
  • 2. Neural Networks Introduced
  • 3. Associative Memory
  • 4. Stochastic Neurons
  • 5. Cybernetic Networks
  • 6. Multilayered Perceptrons
  • 7. Applications
  • 8. More Applications of Neural Networks
  • 9. Network Architecture and Generalization
  • 10. Associative Memory: Advanced Learning Strategies
  • 11. Combinatorial Optimization
  • 12. VLSI and Neural Networks
  • 13. Symmetrical Networks with Hidden Neurons
  • 14. Coupled Neural Networks
  • 15. Unsupervised Learning
  • 16. Evolutionary Algorithms for Learning
  • 17. Statistical Physics and Spin Glasses
  • 18. The Hopfield Network for p/N’ 0
  • 19. The Hopfield Network for Finite p/N
  • 20. The Space of Interactions in Neural Networks
  • 21. Numerical Demonstrations
  • 22. ASSO: Associative Memory
  • 23. ASSCOUNT: Associative Memory for Time Sequences
  • 24. PERBOOL: Learning Boolean Functions with Back-Prop
  • 25. PERFUNC: Learning Continuous Functions with Back-Prop
  • 26. Solution of the Traveling-Salesman Problem
  • 27. KOHOMAP: The Kohonen Self-organizing Map
  • 28. btt: Back-Propagation Through Time
  • 29. NEUROGEN: Using Genetic Algorithms to Train Networks
  • References.