Handbook of Neural Computing Applications

Alianna J. Maren, Craig T. Harston, Robert M. Pap

• Publisher: Academic Press
• Year: 2014
• ISBN:
  9781483264844
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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.

Table of contents

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