1. Artificial Neural Networks An Overview and Analysis

2. Modeling the Human Brain Artificial Neural Networks concentrate on imitating humans rather than acting as rational agents. The goal in ANNs is to imitate the learning that takes place in the brain. The motivation behind this was to find a way for a machine to LEARN.

3. Structure of an ANN The basic function component of an ANN is the unit. These units are connected to each other through links. Each link has a weight associated with it. These weights are the means of long term storage in ANNs. Also, learning is usually accomplished by updating these weights. Some units are connected to the outside world, and are designated as input or output units. Units on the same level make up a layer.

4. ANN Learning An ANN learns when the weights of the links are adjusted. Learning is complete when desired output is very close to actual output. Learning takes place when desired output and actual output are compared. – The difference between the two is measured and adjustments are made to the weights inside the ANN.

5. Network Structures Feedforward – Perceptrons – Multilayered Feedback – Recurrent

6. Feedforward Systems Links are unidirectional Acyclic In a typical, layered Feedforward network, each unit is linked only to units in the next layer.

7. Perceptrons Single layered networks. Perceptron learning is very easy. However, only linearly separable functions can be represented by perceptrons.

8. Optimal Linear Associative Memory Architecture: Single layer Feedforward System.

9. Multilayer Contain one or more layers of “hidden” nodes. Not limited to Linearly Separable functions. Can learn any function There exists a popular method for learning: back-propagation.

10. Maxnet-Hamming Network Architecture: Feedforward Multilayer System

11. Feedback Systems Cyclic Output can be directed back as inputs to previous or same level nodes. Much more complex than a Feedforward system. A Recurrent system is simply a Feedback system with closed loops.

12. Adaptive Resonance Theory Architecture: Bi-directional Feedback System

13. Applications Handwriting Character Recognition English Text Pronunciation Driving – ALVINN (Automated Land Vehicle In a Neural Network): requires 5 minutes of watching a human drive, and 10 minutes of back-propagation. Can drive at speeds of up to 70 mph for about 90 minutes. Classification

14. Multilayer vs. Perceptron Perceptron learns the fastest. Perceptrons have a limited learning capacity Perceptron is too simple for most practical applications.

15. Multilayer vs. Feedback A Feedback System more closely models the brain. Both systems can learn any formula. A Multilayer system uses less overhead. Feedback Systems are a lot more complex.

16. Most Efficient ANN Multilayered Feedforward system – Relative simplicity – Learning Capacity – The Back-Propagation Algorithm is very good.

17. The End Any Questions?