Handbook of Neural Computing Applications
- 1st Edition - January 1, 1990
- Authors: Alianna J. Maren, Craig T. Harston, Robert M. Pap
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 8 3 2 - 4 4 6 0 - 0
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 6 4 8 4 - 4
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… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteHandbook 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.
AcknowlegmentsPreface1 Introduction to Neural Networks 1.0 Overview 1.1 Practical Applications 1.2 The Advantages of Neural Networks 1.3 A Definition of Neural Networks 1.4 Summary References2 History and Development of Neural Networks 2.0 Overview 2.1 Early Foundations 2.2 Promising and Emerging Technology 2.3 Disenchantment 2.4 Innovation 2.5 Re-Emergence 2.6 Current Status 2.7 Summary References3 The Neurological Basis for Neural Computations 3.0 Neuroscience As A Model 3.1 The Single Neuron 3.2 Early Research 3.3 Structural Organization of Biological Neural Systems 3.4 Structurally Linked Dynamics of Biological Neural Systems 3.5 Emergent Properties Arise from the Dynamics of Biological Neural Systems 3.6 Learning in Biological Neural Systems 3.7 Functional Results of Neural Architecture 3.8 Computer Simulations Based on the Brain References4 Neural Network Structures: Form Follows Function 4.0 Overview 4.1 Levels of Structural Description 4.2 Neural Micro-Structures 4.3 Neural Meso-Structures 4.4 The Macro-Structure 4.5 Summary5 Dynamics of Neural Network Operations 5.0 Overview 5.1 Typical Network Dynamics 5.2 Energy Surfaces and Stability Criterion 5.3 Network Structures and Dynamics References6 Learning Background for Neural Networks 6.0 Overview 6.1 Intelligence: An Operational Definition 6.2 Learning and Conditioning 6.3 Learned Performance 6.4 Motivation 6.5 Summary References7 Multilayer Feedforward Neural Networks I: Delta Rule Learning 7.0 Overview 7.1 Introduction 7.2 The Perceptron Network 7.3 Adaline and Madaline Neural Networks 7.4 The Back-Propagation Network References8 Multilayer Feedforward Neural Networks II: Optimizing Learning Methods 8.0 Overview 8.1 The Boltzmann Machine 8.2 The Cauchy Machine: A Refinement of the Boltzmann Machine 8.3 Summary References9 Laterally-Connected, Autoassociative Networks 9.0 Overview 9.1 Introduction to Association Networks 9.2 Auto Associative Networks 9.3 The Hopfield/Tank Network 9.4 The Brain-State-In-A-Box Network 9.5 Kanerva's Sparse Distributed Memory Network 9.6 Summary References10 Vector-Matching Networks 10.0 Overview 10.1 Introduction 10.2 The Kohonen Learning Vector Quantization Network 10.3 The Self-Organizing Topology-Preserving Map 10.4 Summary References11 Feedforward/Feedback (Resonating) Heteroassociative Networks 11.0 Chapter Overview 11.1 Introduction 11.2 The Carpenter/Grossberg Adaptive Resonance Theory Network 11.3 Bidirectional Associative Memories and Related Networks 11.4 Summary References12 Multilayer Cooperative/Competitive Networks 12.0 Overview 12.1 Introduction 12.2 Competitive Learning Networks 12.3 Masking Fields 12.4 The Boundary Contour System 12.5 Hierarchical Scene Structures 12.6 The Neocognitron 12.7 Summary References13 Hybrid and Complex Networks 13.0 Overview 13.1 Introduction 13.2 Hybrid Networks: The Hamming Network and the Counter-Propagation Network 13.3 Neural Networks Operating in Parallel 13.4 Hierarchies of Similar Networks 13.5 Systems of Different Types of Neural Networks 13.6 Systems of Networks are Useful for Adaptive Control 13.7 Summary References14 Choosing A Network: Matching the Architecture to the Application 14.0 Chapter Overvie 14.1 When to use A Neural Network 14.2 What Type of Network? 14.3 Debugging, Testing, and Verifying Neural Network Codes 14.4 Implementing Neural Networks References15 Configuring and Optimizing the Back-Propagation Network 15.0 Overview 15.1 Issues in Optimizing and Generalizing Feedforward Networks 15.2 Micro-Structural Considerations 15.3 Meso-Structural Considerations 15.4 Optimizing Network Dynamics 15.5 Learning Rule Modifications 15.6 Modifications to Network Training Schedules and Datasets References16 Electronic Hardware Implementations 16.0 Overview 16.1 Analog Implementations 16.2 Digital Neural Network Chips 16.3 Hybrid Neural Network Chips 16.4 Method for Comparing Neural Network Chips 16.5 Summary Further Reading in Neural Network Hardware Implementation17 Optical Neuro-Computing 17.0 Overview 17.1 Historical Introduction of Optical Neurocomputing 17.2 Review of Learning Algebras and Architectures 17.3 Associative Memory vs. Wiener Filter and Self-Organization-Map vs. Kalman Filters 17.4 Optical Implementations of Neural Networks 17.5 Comparison Between Electronic and Optic Implementations of Neural Networks 17.6 Hybrid Neurocomputing 17.7 Application to Pattern Recognition and Image Processing 17.8 The Superconducting Mechanism 17.9 The Super-Triode 17.10 The Super-Triode Neurocomputer 17.11 Wave-Front Imaging Telescope with a Focal Plane Array of Super-Triodes 17.12 Space-Borne In-Situ Smart Sensing with Neurocomputing 17.13 Conclusion Bibliography18 Neural Networks for Spatio-Temporal Pattern Recognition 18.0 Overview 18.1 Creating Spatial Analogues of Temporal Patterns 18.2 Neural Networks with Time Delays 18.3 Storing and Generating Temporal Patterns Via Recurrent Connections 18.4 Using Neurons with Time-Varying Activations and Summing Information Over Time Intervals 18.5 Neural Nets which have Short-Term and Long-Term Memories 18.6 Frequency Coding in Neural Networks 18.7 Networks with Combinations of Different Temporal Capabilities 18.8 Summary References19 Neural Networks for Medical Diagnosis 19.0 Overview 19.1 Introduction 19.2 Prospects for Neural Networks in Medicine 19.3 Potential Niches for Neural Network Diagnostic Aids 19.4 Factors Affecting Physician Acceptance 19.5 Diagnostic Network Design Considerations 19.6 Existing Neural Networks for Medical Diagnosis 19.7 Existing Neural Networks for Prognosis and Treatment 19.8 Summary References20 Neural Networks for Sonar Signal Processing 20.0 Overview 20.1 Introduction 20.2 Sonar Signal Processing Systems 20.3 Beam-Forming and Bearing Estimation 20.4 Noise Cancellation 20.5 Feature Extraction 20.6 Detection and Classification 20.7 Summary References21 Fault Diagnosis 21.0 Introduction: Making Diagnostics Work in the Real World — A Few Tricks 21.1 Overview 21.2 Techniques 21.3 Applications 21.4 Power Generation Facilities 21.5 Summary References22 Neurocontrol and Related Techniques 22.0 Overview 22.1 Introduction 22.2 The Five Basic Designs 22.3 Areas of Application 22.4 Supervised Learning and Expert Systems 22.5 Further Details on the Five Basic Designs 22.6 Robust Neuro-Identification References23 Application of Neural Networks to Robotics 23.0 Overview 23.1 Neurology Applied to Robotics 23.2 Neural Networks Applied to Robotic Tasks 23.3 Technological Considerations 23.4 Summary References24 Business with Neural Networks 24.0 Introduction 24.1 Marketing 24.2 Operations Management 24.3 Financial Analysis 24.4 Where is Accounting-Auditing? 24.5 Summary References25 Neural Networks for Data Compression and Data Fusion 25.0 Overview 25.1 Introduction 25.2 Neural Networks for Data Compression and Dimensionality Reduction 25.3 Neural Networks for Image Data Compression 25.4 Neural Network Methods for Multisource Information Correlation/Fusion References26 Data Communications 26.0 Overview 26.1 Network Management 26.2 ISDN Communications Network Control 26.3 Network Switching 26.4 Data Routing 26.5 Data Interpretation 26.6 Optical Implementations 26.7 Adaptive Filter 26.8 Quadrature Amplitude Modulation 26.9 Local and Wide Area Networks References27 Neural Networks for Man/Machine Systems 27.0 Overview 27.1 Adaptive Interfaces 27.2 Adaptive Aiding 27.3 Neural Networks to Emulate Human Performance 27.4 Neural Networks for Bioengineering 27.5 Summary References28 Capturing The Future: Neural Networks in the Year 2000 and Beyond 28.0 Introduction 28.1 Prediction 1 28.2 Prediction 2 28.3 Prediction 3 28.4 Prediction 4 28.5 Prediction 5 28.6 Prediction 6 28.7 Prediction 7 28.8 Prediction 8 28.9 Prediction 9 28.10 Prediction 10 ReferencesIndex
- No. of pages: 470
- Language: English
- Edition: 1
- Published: January 1, 1990
- Imprint: Academic Press
- Paperback ISBN: 9781483244600
- eBook ISBN: 9781483264844
Read Handbook of Neural Computing Applications on ScienceDirect