neural network

abt neural network & it's application for seminar

1. Neural Network Guided by Chapala Maharana Submitted By- Nilmani Singh Branch-CSE Regd. No.-0601289251

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 of computing, based on the interaction of multiple connected 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

5. Neurons Biologica l Artificial

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 Controls when 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 Static( Feedforward ) (unit delay operator z -1 implies dynamic system) Classification Based On Topology Dynamic(Feedback) 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 After many epochs convergence to a local minimum

11. Unsupervised Learning No help from the outside No training data, no information available on 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. Pattern recognition Investment analysis Control systems & monitoring Mobile computing Marketing and financial applications Forecasting – sales, market research, meteorology Neural Network Applications

14. Advantages: A neural network can perform tasks that a linear program can not. When an element of the neural network fails, it can continue without any problem by their parallel nature. A neural network learns and does not need to be reprogrammed. It can be implemented in any application. It can be implemented without any problem

15. Disadvantages: The neural network needs training to operate. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated. Requires high processing 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 ?