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September 15, 2010Priti Srinivas Sajja Neural Network and its Applications Dr. Priti Srinivas Sajja Associate Professor G H Patel P G Department of Computer.

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Presentation on theme: "September 15, 2010Priti Srinivas Sajja Neural Network and its Applications Dr. Priti Srinivas Sajja Associate Professor G H Patel P G Department of Computer."— Presentation transcript:

1 September 15, 2010Priti Srinivas Sajja Neural Network and its Applications Dr. Priti Srinivas Sajja Associate Professor G H Patel P G Department of Computer Science and Technology Sardar Patel University, Vallabh Vidyanagar 388 120, India 1

2 Priti Srinivas Sajja Introduction and Contact Information Name: Dr. Priti Srinivas Sajja Communication: Email : Mobile : +91 9824926020 URL : Academic qualifications : Ph. D in Computer Science Thesis title: Knowledge-Based Systems for Socio- Economic Development Subject area of specialization : Artificial Intelligence Publications : 84 in International/ National Conferences, Journals & Books September 15, 20102

3 Priti Srinivas Sajja Lecture Plan Introduction and Background Artificial Intelligence and Data Pyramid Connectionist and Symbolic Systems Biological Neuron and Artificial Neuron Characteristics of Artificial Neural Network Architecture of ANN Hopfield Model and Parallel Relaxation Single Perceptron and Linearly Separable Models Multi Layer Perceptron and Back Propogation Supervised Learning and Training Data Example ANNs Hybrid Systems Conclusion and References September 15, 20103

4 Priti Srinivas Sajja Artificial Intelligence “Artificial Intelligence(AI) is the study of how to make computers do things at which, at the moment, people are better ” Elaine Rich, Artificial Intelligence, McGraw Hill Publications, 1986 September 15, 20104

5 Priti Srinivas Sajja Knowledge-Based Systems (Symbolic Representation of Knowledge) K K Knowledge-Based Systems (KBS) are Productive Artificial Intelligence Tools working in a narrow domain. September 15, 20105 Data Information Knowledge Wisdom Understanding Experience Novelty Researching Absorbing Doing Interacting Reflecting Raw Data through fact finding Concepts Rules Heuristics and models Convergence from data to intelligence

6 Priti Srinivas Sajja Structure of KBS Knowledge Base Inference Engine User Interface Explanation/ Reasoning Self Learning September 15, 2010 6

7 Priti Srinivas Sajja Limitations of Symbolic Systems Nature of knowledge Hard to characterize Voluminous Dynamic Knowledge acquisition Fact finding methods support only Tacit and higher level knowledge Multiple experts Knowledge representation Limited knowledge structures support KBS development models Only SAD/SE guidelines and a few quality metrics Large size of knowledge base September 15, 20107

8 Priti Srinivas Sajja Biological and Artificial Neurons 8

9 Priti Srinivas Sajja Artificial Neural Network (towards Connectionist Representation of Knowledge) Objective: Not to mimic brain functionality but to receive inspiration from the fact about how brain is working. Characterized by: A large number of very simple neuron like processing elements. A large number of weighted connection between the elements. This weights encode the knowledge of a network. Highly parallel and distributed control. Emphasis on learning internal representation automatically. September 15, 20109

10 Priti Srinivas Sajja Architectures of ANN Hopfield network Perceptron Multi-layer Perceptron Self Organizing Network etc. September 15, 201010

11 Priti Srinivas Sajja In a Hopfield network, all processing units/elements are in two states either active or inactive. Units are connected to each other with weighted Connections. A positively weighted connection indicates that the units tend to active each other. A negative connection allows an active unit to deactivate a neighbouring unit. Active Inactive +1 +3 -2 +1 +2 +1 September 15, 201011 A Simple Hopfield Network

12 Priti Srinivas Sajja Parallel Relaxation Active Inactive +1 +3 -2 +1 +2 +1 A random unit is chosen. If any of its neighbours are active, the unit computes the sum of weights on the connections to those active neighbours. If the sum is positive, the unit becomes active else new random unit is chosen. This process will continue till the network become stable. That is no unit can change its status. This process is known as parallel relaxation. September 15, 201012

13 Priti Srinivas Sajja Perceptron X1X1 X2X2 ∑W i X i T W1W1 W2W2 Mom (0.3) Dad (0.5) ∑W i X i 0.6 0.4 Going to Army: To Be or not to Be? Importance to Mom Importance to Dad = 0.3*0.6 + 0.5*0.4 = 0.18 +0.20 = 0.38 which is < 0.6 September 15, 201013

14 Priti Srinivas Sajja Logical Gate AND and OR X1X1 X2X2 ∑W i X i 0.6 0.5 X1X1 X2X2 X 1 AND X 2 000*0.5 + 0*0.5 = 0 <0.6  0 010*0.5 + 1*0.5 = 0 <0.6  0 101*0.5 + 0*0.5 = 0.5 <0.6  0 111*0.5 + 1*0.5 = 1 >0.6  1 Logical AND Truth Table (1,1) September 15, 201014

15 Priti Srinivas Sajja Logical Gate AND and OR (1,1) September 15, 201015

16 Priti Srinivas Sajja X1X1 X2X2 1  ƒ -1.5 1.0 -9.0 X1X1 X2X2 1  ƒ -0.5 1.0 Kw o w 1 w 2 10.41-.17.14 100.22-.14.11 300-.1-.008.07 635-.49-.1.14 Kw o w 1 w 2 10.41-.17.14 100.22-.14.11 300-.1-.008.07 635-.49-.1.14 A Perception Learning to Solve a Classification Problem September 15, 201016

17 Priti Srinivas Sajja O1O1 h1h1 h3h3 h2h2 hBhB 1x1x1 x2x2 x3x3 x4x4 xAxA OcO2O2 Output Layer Hidden Layer Input Layer W2 ij w1 ij Fully connected, multi layered, feed-forward network structure ……This network has three layers but there may be many. September 15, 201017

18 Priti Srinivas Sajja Examples of Multilayer Perceptron X1X1 X2X2 H1H1 H2H2 Two digited One degited Three digited O2O2 O1O1 O3O3 Training Set Data 2, 3, 1, 0, 0 10, 10, 0, 1, 0 90, 90, 0, 0, 1 …… ….. … X 1 and X 2 are two one /two digited positive numbers September 15, 201018

19 Priti Srinivas Sajja Example of Multilayer Perceptron X1X1 X2X2 H1H1 H2H2 Nokia Base Reliance Nokia Higher O2O2 O1O1 O3O3 Sony Ericsson Blackberry X3X3 X4X4 H3H3 H4H4 O4O4 O5O5 Budget Color Camera Radio Training Set Data 2, 3, 0, 0, 1, 0, 0, 0, 0 6, 6, 7, 5, 0, 0, 1, 0, 0 8, 8, 8, 8, 0, 0, 0, 0, 1 …… ….. … September 15, 201019

20 Priti Srinivas Sajja Example of Multilayer Perceptron X1X1 X2X2 H1H1 H2H2 Job B Job A Job C O2O2 O1O1 O3O3 X3X3 X4X4 H3H3 H4H4 Salary InterestDistanceFuture Design and Train the above structure using your own choices considering the following practical situations: A.Job At Bengaluru, salary Rs.30, 000 per month of your field B.Job At USA, salary Rs.80, 000 per month of other field C.Job At Anand, salary Rs.25, 000 per month of your field September 15, 201020

21 Priti Srinivas Sajja ANN to Determine Aptitude of Users September 15, 201021

22 Priti Srinivas Sajja Source: A method was developed to provide a generic design tool for estimating wave overtopping discharges for a very wide range of coastal structures. September 15, 201022

23 September 15, 2010Priti Srinivas Sajja Sprayer Sensor and Nozzle Element (Zhang, Yang, & El- Faki, 1994) ANN Simulator 23

24 September 15, 2010Priti Srinivas Sajja Optimization, function approximation, time series prediction, and modeling Classification, pattern matching and recognition (three-dimensional object recognition), novelty detection, and sequential decision-making Data processing (including filtering, clustering, blind source separation and compression), data mining, data compression (speech signal, image—for example, faces), and data validations System identification and control (vehicle control, process control) and signal processing Game playing and decision making (backgammon, chess, racing) Sequence recognition (gesture, handwritten text recognition) Medical diagnosis (for example, hepatitis or storing medical records based on case information) Financial applications (automated trading systems, time series analysis, stock market prediction) and customer research Cognitive science, neurobiology, and the study of models of how the brain works Biological neural networks, which communicate through pulses and use the timing of the pulses to transmit information and perform computations Integration of fuzzy logic and neural networks for applications in automotive engineering, screening applicants for jobs, controlling a crane, or monitoring a medical condition like glaucoma Robotics (navigation and vision recognition) Speech production and recognition Vision (face recognition, edge detection, visual search engines) New topologies and hardware implementations New learning algorithms In hybrid systems and soft computing—for example, rule extraction for fuzzy systems, self-evolving ANNs, and neuro-fuzzy systems Applications of ANN 24

25 Priti Srinivas Sajja Hybrid Systems/Soft Computing Neural network Probabilistic reasoning Genetic algorithms Fuzzy logic Evolving neuro- fuzzy systems Genetic bayesian network Soft computing Neuro-fuzzy and fuzzy neural network Genetic fuzzy Modular rough networks Application Layer 1 Application Layer 2 Constituents of soft computing September 15, 201025

26 Priti Srinivas Sajja Strength of a Hybrid Soft Computing System Fuzzy logic Approximate reasoning, impressions FieldStrength Offered Artificial neural network Learning and implicit knowledge representation Genetic algorithm Natural evolution and optimization Probabilistic reasoning Uncertainty September 15, 201026

27 Priti Srinivas Sajja Hybrid Applications Data ANN Fuzzy Sets Fuzzy Rules FIS Output    Y1Y2YNY1Y2YN X1X2XnX1X2Xn Fu zz y Int erf ac e (a) fuzzy neural model(b) co-operative neuro-fuzzy model Data ANN FIS Output (c) concurrent neuro-fuzzy model (d) hybrid neuro-fuzzy model Approaches of neuro-fuzzy computing Neuro-fuzzy Systems September 15, 201027

28 Priti Srinivas Sajja Reference Knowledge-Based Systems Rajendra Akerkar and Priti Srinivas Sajja Jones & Bartlett Publishers Sudbury, MA, USA (2009) September 15, 201028

29 Priti Srinivas Sajja To the CVM and ISTAR Family Vidyanagar, Gujarat, India September 15, 201029

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