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- 2. Contents .Introduction to Neural Network .Neurons .Activation Function .Types of Neural Network .Learning In Neural Networks .Application of Neural Network .Advantages of Neural Network .Disadvantages Of Neural Network
- 3. Neural Networks • A method ofcomputing, based on the interactionof multipleconnected processing elements. • A powerful technique to solve many real world problems. • The ability to learn from experience in order to improve their performance. • Ability to deal with incomplete information
- 4. • • • • Biological approach to AI Developed in 1943 Comprised of one or more layers of neurons Several types, we’ll focus on feed-forward and feedback networks Basics Of Neural Network
- 6. Neural Network Neurons • • • • Receives n-inputs Multiplies each input by its weight Applies activation function to the sum of results Outputs result
- 7. Activation Functions • • • Controlswhen unit is “active” or “inactive” Threshold function outputs 1 when input is positive and 0 otherwise Sigmoid function = 1 / (1 + e-x)
- 8. Neural Network types can be classified based on following attributes: • Connection Type - Static (feedforward) - Dynamic (feedback) • Topology - Single layer - Multilayer - Recurrent • Learning Methods - Supervised - Unsupervised - Reinforcement Types of Neural Networks
- 9. Classification Based On Connection Types (unit delay operator z-1 implies dynamic system) •Static(Feedforward) • Dynamic(Feedback) Classification Based On Topology • Single layer • Multilayer • Recurrent
- 10. Classification Based On Learning Method • Supervised • Unsupervised • Reinforcement Supervised learning • Each training pattern: input + desired output • At each presentation: adapt weights • Aftermany epochs convergence to a local minimum
- 11. Unsupervised Learning • • • • • No help from the outside No training data,no information availableon the desired output Learning by doing Used to pick out structure in the input: • Clustering • Reduction of dimensionality compression Example: Kohonen’s Learning Law
- 12. Reinforcement learning • • • • Teacher: training data The teacher scores the performance of the training examples Use performance score to shuffle weights ‘randomly’ Relatively slow learning due to ‘randomness’
- 13. Neural Network Applications • Pattern recognition • Investment analysis • Control systems & monitoring • Mobile computing • Marketing and financial applications • Forecasting – sales, market research, meteorology
- 14. Advantages: • A neural network can perform tasks that a linear program can not. • When anelementoftheneural network fails, it can continue withoutany problemby their parallel nature. • A neural network learns and does not need tobe reprogrammed. • It can be implemented in any application. • It can be implemented without any problem
- 15. Disadvantages: • Theneural network needs training to operate. • The architectureof a neural network isdifferentfrom the architecture of microprocessors therefore needs to beemulated. • Requires highprocessing time for large neural networks.
- 16. Conclusions • • • Neural networks provide ability to provide more human-like AI Takes rough approximation and hard-coded reactions out of AI design (i.e. Rules and FSMs) Still require a lot of fine- tuning during development
- 17. References • • • • • Neural Networks, Fuzzy Logic, and Genetic Algorithm ( synthesis and Application) S.Rajasekaran, G.A. Vijayalakshmi Pai, PHI Neuro Fuzzy and Soft Computing, J. S. R. JANG,C.T. Sun, E. Mitzutani, PHI Neural Netware, a tutorial on neural networks Sweetser, Penny. “Strategic Decision-Making with Neural Networks and Influence Maps”, AI Game Programming Wisdom 2, Section 7.7 (439 – 46) Russell, Stuart and Norvig, Peter. Artificial Intelligence: A Modern Approach, Section 20.5 (736 – 48)
- 18. THANK YOU
- 19. QUESTIONS ?