1. NEURAL NETWORKS

4. An extremely simplified model of the brain !

5. Composed of many “neurons” that co-operate to perform the desired function

6. Unit of Neural network

7. A firing rule determines how one calculates whether a neuron should fire for any input pattern.

10. ► First neural network with the ability to learn ► Made up of only input neurons and output neurons ► Input neurons typically have two States: ON and OFF ► Output neurons use a simple Threshold activation function ► In basic form, can only solve linear problems ► Limited applications

11. ► Uses Supervised Training. ► If output is not correct outputs are corrected according to the formula ► w new =w old + (Desired-output)*input

12. L inear function Threshold function S igmoidal function

13. ► Supplies the neural network with inputs and the desired outputs Supervised Training

14. ► Only supplies inputs ► The neural network adjusts its own weights so that similar inputs Cause similar outputs Unsupervised Training

15. Most common method of obtaining the many weights in the networkz based on minimizing the error of the network using the derivatives of the error function

16. ► S imple Most common measure of error is the mean square error: E = (target – output)2 ► Slow ► Prone to local minima issues

20. Let us take = 1.5 then, As per the inequalities we get, w1=1 w2=1

35. Financial Analysis Financial economic forecasting Bankruptcy Predictions Signature Analysis Process Control Optical Character Recognition Robotics Medical Diagnosis Quality Control Systems

37. So we have seen their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. We will only get the best of them when they are integrated with computing, AI, fuzzy logic and related subjects.

38. Detecting head orientation by Paul Fitzpatrick, D. J. Beymer. Face recognition under varying pose. In Computer Vision and Pattern Recognition, pages 756{761, June 1994. I. Shimizu, Z. Zhang, S. Akamatsu, and K. Deguchi. Head pose determination from one image using a generic model. In 4th International Conference on Automatic Face and Gesture Recognition, pages 100{105, April 1998. A SIMPLE AND ROBUST METHOD FOR MOVING TARGET TRACKING, Gaetano Baldini*, Paola Campadelli, Dario Cozzi, Raffaella Lanzarotti, Via Comelico, 39/41 20135 Milano, Italy Haritaoglu, D.Harwood, L.S. Davis – “W4: Real-Time Surveillance of People and Their Activities” –IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, N.8, August 2000

39. Websites for Data http://uhaweb.hartford.edu/compsci/neural-networks-tutorial.html http://ai.arizona.edu/papers/mlir93/mlir93.html http://www.webopedia.com/TERM/N/neural_network.html http://ftp.sas.com/pub/neural/FAQ.html http://www.webopedia.com/TERM/N/neural_network.html http://tralvex.com/pub/nap/ http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html http://www.cs.utoronto.ca/~dt/siggraph96-course/ http://www.nd.com/neurosolutions/products/ns/whatisNN.html http://www.nd.com/appcornr/purpose.htm http://www.cs.utoronto.ca/~dt/siggraph96-course http://www.ip-atlas.com/pub/rover/main.htm