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Neural Networks Artificial Intelligence and
CONTENTS INTRODUCTION HUMAN AND ARTIFICIAL NEURONS AN ENGINEERING APPROACH ARCHITECTURE OF NEURAL NETWORKS THE LEARNING PROCESS APPLICATIONS CONCLUSION
INTRODUCTION What is a Neural Network? It is an information processing paradigm that is inspired by the way biological nervous system, such as brain, process information. Historical Background. Why we use Neural Network? Adaptive learning, Self-Organisation Neural Networks Vs Conventional Computers.
Human and Artificial Neurons How the Human Brain Learns? Components of a neurons
From Human Neurons to Artificial Neurons
An Engineering Approach A simple Neuron
Firing Rules:  Example: After Applying Firing X1: 0 0 0 0 1 1 1 1 X2: 0 0 1 1 0 0 1 1 X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0/1 0/1 0/1 1 0/1 1 X1: 0 0 0 0 1 1 1 1 X2: 0 0 1 1 0 0 1 1 X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0 0/1 0/1 1 1 1
Architecture Of Neural Networks Feed-Forward Networks Feed-Back Networks Network Layers An example of a simple feed forward network An example of a complicated network
The Learning Process Association Mapping Auto-association Hetero-association Nearest neighbour recall Interpolative recall  Regularity Detection Types of NN based on Weights Fixed Networks Adaptive Networks
Transfer Function Linear Threshold Sigmoid Back-Propagation Algorithm Types Of Learning Supervised Learning Unsupervised Learning
Applications Sales Forecasting Industrial Process Control Customer Search Data Validation Risk Management Target Marketing
Neural Networks in Medicine: Modelling and diagnosing Cardiovascular system Electronic Noses Instant Physician Neural Networks in Business: Marketing Credit Evaluation Forecasting the Demand and Sales
Conclusion The computing world has a lot to gain from neural networks. They are also well suited for real time systems. NN also contribute to other areas of research such as neurology and pschology.
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Neural networks

  • 1. Neural Networks Artificial Intelligence and
  • 2. CONTENTS INTRODUCTION HUMAN AND ARTIFICIAL NEURONS AN ENGINEERING APPROACH ARCHITECTURE OF NEURAL NETWORKS THE LEARNING PROCESS APPLICATIONS CONCLUSION
  • 3. INTRODUCTION What is a Neural Network? It is an information processing paradigm that is inspired by the way biological nervous system, such as brain, process information. Historical Background. Why we use Neural Network? Adaptive learning, Self-Organisation Neural Networks Vs Conventional Computers.
  • 4. Human and Artificial Neurons How the Human Brain Learns? Components of a neurons
  • 5. From Human Neurons to Artificial Neurons
  • 6. An Engineering Approach A simple Neuron
  • 7. Firing Rules: Example: After Applying Firing X1: 0 0 0 0 1 1 1 1 X2: 0 0 1 1 0 0 1 1 X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0/1 0/1 0/1 1 0/1 1 X1: 0 0 0 0 1 1 1 1 X2: 0 0 1 1 0 0 1 1 X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0 0/1 0/1 1 1 1
  • 8. Architecture Of Neural Networks Feed-Forward Networks Feed-Back Networks Network Layers An example of a simple feed forward network An example of a complicated network
  • 9. The Learning Process Association Mapping Auto-association Hetero-association Nearest neighbour recall Interpolative recall Regularity Detection Types of NN based on Weights Fixed Networks Adaptive Networks
  • 10. Transfer Function Linear Threshold Sigmoid Back-Propagation Algorithm Types Of Learning Supervised Learning Unsupervised Learning
  • 11. Applications Sales Forecasting Industrial Process Control Customer Search Data Validation Risk Management Target Marketing
  • 12. Neural Networks in Medicine: Modelling and diagnosing Cardiovascular system Electronic Noses Instant Physician Neural Networks in Business: Marketing Credit Evaluation Forecasting the Demand and Sales
  • 13. Conclusion The computing world has a lot to gain from neural networks. They are also well suited for real time systems. NN also contribute to other areas of research such as neurology and pschology.