Artificial Neural Networks Technology
Table of Contents
Postscript Version
[2.8MB] -
Text Version
][193k]
Title Page
List of Tables and Figures
1.0 Introduction and Purpose
2.0 What are Artificial Neural Networks?
2.1 Analogy to the Brain
2.2 Artificial Neurons and How They Work
2.3 Electronic Implementation of Artificial Neurons
2.4 Artificial Network Operations
2.5 Training an Artificial Neural Network
2.5.1 Supervised Training.
2.5.2 Unsupervised, or Adaptive Training.
2.6 How Neural Networks Differ from Traditional Computing and Expert Systems
3.0 History of Neural Networks
4.0 Detailed Description of Neural Network Components and How They Work
4.1 Major Components of an Artificial Neuron
4.2 Teaching an Artificial Neural Network
4.2.1 Supervised Learning.
4.2.2 Unsupervised Learning.
4.2.3 Learning Rates.
4.2.4 Learning Laws.
5.0 Network Selection
5.1 Networks for Prediction
5.1.1 Feedforward, Back-Propagation.
5.1.2 Delta Bar Delta.
5.1.3 Extended Delta Bar Delta.
5.1.4 Directed Random Search.
5.1.5 Higher-order Neural Network or Functional-link Network.
5.1.6 Self-Organizing Map into Back-Propagation.
5.2 Networks for Classification
5.2.1 Learning Vector Quantization.
5.2.2 Counter-propagation Network.
5.2.3 Probabilistic Neural Network.
5.3 Networks for Data Association
5.4 Networks for Data Conceptualization
5.5 Networks for Data Filtering
5.5.1 Recirculation.
6.0 How Artificial Neural Networks Are Being Used
6.1 Language Processing
6.2 Character Recognition
6.3 Image (data) Compression
6.4 Pattern Recognition
6.5 Signal Processing
6.6 Financial
6.7 Servo Control
6.8 How to Determine if an Application is a Neural Network Candidate
7.0 Emerging Technologies
7.1 What Currently Exists
7.1.1 Development Systems.
7.1.2 Hardware Accelerators.
7.1.3 Dedicated Neural Processors.
7.2 What the Next Developments Will Be
8.0 Summary
9.0 References
