Handbook of Neural Computing Applications
eBook - PDF

Handbook of Neural Computing Applications

  1. 470 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Handbook of Neural Computing Applications

About this book

Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.

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Yes, you can access Handbook of Neural Computing Applications by Alianna J. Maren,Craig T. Harston,Robert M. Pap in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Front Cover
  2. Handbook of Neural Computing Application
  3. Copyright Page
  4. Table of Contents
  5. ACKNOWLEDGMENTS
  6. PREFACE
  7. CHAPTER 1. INTRODUCTION TONEURAL NETWORKS
  8. CHAPTER 2. HISTORY ANDDEVELOPMENT OFNEURAL NETWORKS
  9. CHAPTER 3. THE NEUROLOGICALBASIS FOR NEURAL COMPUTATIONS
  10. CHAPTER 4. NEURAL NETWORK STRUCTURES: FORM FOLLOWS FUNCTION
  11. CHAPTER 5. DYNAMICS OF NEURAL NETWORK OPERATIONS
  12. CHAPTER 6. LEARNING BACKGROUND FOR NEURAL NETWORKS
  13. CHAPTER 7. MULTILAYER FEEDFORWARD NEURAL NETWORKS I: DELTA RULE LEARNING
  14. CHAPTER 8. MULTILAYER FEEDFORWARD NEURAL NETWORKS II: OPTIMIZING LEARNING METHODS
  15. CHAPTER 9. LATERALLY-CONNECTED, AUTOASSOCIATIVE NETWORKS
  16. CHAPTER 10. VECTOR-MATCHING NETWORKS
  17. CHAPTER 11. FEEDFORWARD/FEEDBACK (RESONATING)HETEROASSOCIATIVE NETWORKS
  18. CHAPTER 12. MULTILAYER COOPERATIVE/COMPETITIVE NETWORKS
  19. CHAPTER 13. HYBRID AND COMPLEX NETWORKS
  20. CHAPTER 14. CHOOSING A NETWORK:MATCHING THE ARCHITECTURE TO THEAPPLICATION
  21. CHAPTER 15. CONFIGURING AND OPTIMIZING THEBACK-PROPAGATION NETWORK
  22. CHAPTER 16. ELECTRONIC HARDWARE IMPLEMENTATIONS
  23. CHAPTER 17. OPTICAL NEURO-COMPUTING
  24. CHAPTER 18. NEURAL NETWORKS FOR SPATIO-TEMPORAL PATTERN RECOGNITION
  25. CHAPTER 19. NEURAL NETWORKS FORMEDICAL DIAGNOSIS
  26. CHAPTER 20. NEURAL NETWORKS FOR SONAR SIGNAL PROCESSING
  27. CHAPTER 21. FAULT DIAGNOSIS
  28. CHAPTER 22. NEUROCONTROL AND RELATED TECHNIQUES
  29. CHAPTER 23. APPLICATION OF NEURAL NETWORKS TO ROBOTICS
  30. CHAPTER 24. BUSINESS WITH NEURAL NETWORKS
  31. CHAPTER 25. NEURAL NETWORKS FORDATA COMPRESSION AND DATA FUSION
  32. CHAPTER 26. DATA COMMUNICATIONS
  33. CHAPTER 27. NEURAL NETWORKS FOR MAN/MACHINE SYSTEMS
  34. CHAPTER 28. CAPTURING THE FUTURE:NEURAL NETWORKS IN THE YEAR 2000 AND BEYOND
  35. INDEX
  36. ABOUT THE AUTHORS