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Neural Nets Applications

Neural Nets Applications. Introduction. Outline(1/2). What is a Neural Network? Benefit of Neural Networks Structural Levels of Organization in the Brain Models of a Neuron Network Architectures Artificial Intelligence and Neural Networks. Outline(2/2). Existing Applications

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Neural Nets Applications

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  1. Neural Nets Applications Introduction

  2. Outline(1/2) • What is a Neural Network? • Benefit of Neural Networks • Structural Levels of Organization in the Brain • Models of a Neuron • Network Architectures • Artificial Intelligence and Neural Networks

  3. Outline(2/2) • Existing Applications • Possible Applications • Experiment I • Experiment II • Other names for Neural Networks • Who are the key player?

  4. What is a Neural Networks(1/5) •  Neural networks technology is not trying to produce biological machine •  but is trying to mimic nature’s approach in order to mimic some of nature’s capabilities.

  5. What is a Neural Networks (2/5) • Definition: • A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use.

  6. What is a Neural Networks (3/5) • It resembles the brain in two respects: • Knowledge is acquired by the network through a learning process. • Interneuron connection strengths known as synaptic weight are used to store the knowledge.

  7. What is a Neural Networks (4/5) • The Human Brain: • Five to six orders of magnitude slower than silicon logic gates • With 60 trillion synapses or connections • A highly complex, nonlinear, and parallel computer. • Figure 1.1

  8. What is a Neural Networks (5/5)

  9. Benefits of Neural Networks (1/2) • Nonlinearity • Input-Output Mapping • Adaptivity • Evidential Response • Contextual Information

  10. Benefits of Neural Networks (2/2) • Fault Tolerance • Implementability • Uniformity of Analysis and Design • Neurobiological Analogy

  11. Structural Levels of Organization in the Brain (1/3) • Figure 1.2 • Figure 1.3

  12. Structural Levels of Organization in the Brain (2/3)

  13. Structural Levels of Organization in the Brain (3/3)

  14. Models of a Neuron (1/6) • Figure 1.4 • Three basic elements of the neuron model: • A set of synapses or connecting links, each of which is characterized by a weight or strength of its own. • An adder for summing the input signals, weighted by the respective synapses of the neuron; the operations described here constitute a linear combiner. • An activation function for limiting the amplitude of the output of a neuron.

  15. Models of a Neuron (2/6)

  16. Models of a Neuron (3/6) • Mathematical terms: where: xj: input signals wkj: synaptic weights uk: linear combiner output θk:: threshold f() : activation function yk: output signal

  17. Models of a Neuron (4/6) 4. Types of activation function: a. Threshold function

  18. Models of a Neuron (5/6) 4. Types of activation function: b. Piecewise-linear function

  19. Models of a Neuron (6/6) 4. Types of activation function: c. Sigmoid Function

  20. Network Architecture (1/5) 1. single-layer feedforward network

  21. Network Architecture (2/5) 2. Multilayer feedforward network (fully connected

  22. Network Architecture (3/5) 2. Multilayer feedforward network (partially connected

  23. Network Architecture (4/5) 3. Recurrent networks

  24. Network Architecture (5/5) 4. Lattice Structures

  25. Artificial Intelligence and Neural Networks (1/5) AI system

  26. Artificial Intelligence and Neural Networks (2/5) a. Representation - use a language of symbol structures to represent both general knowledge about a problem domain of interest and specific knowledge about the solution to the problem.

  27. Artificial Intelligence and Neural Networks (3/5) b. Reasoning - the ability to solve the problems - be able to express and solve a broad range of problems and problem types. - be able to make explicit and inplicit information known to it - have a control mechanism that determines which operations to apply to a particular problem.

  28. Artificial Intelligence and Neural Networks (4/5) c. Learning - Fig 1.27 - Inductive, rules are from raw data and experience - Deductive, rules are used to determine specific facts

  29. Artificial Intelligence and Neural Networks (5/5)

  30. Existing Applications(1/4) • Long distance echo adaptive fitteradaptive noise canceling -- ADALINE • Mortgage risk evaluator • Bomb sniffer --SNOOPE -- JFK airport

  31. Existing Applications(2/4) • 4. Process Monitor • -- GTE used in fluorescent bulb plant. • -- To determine optimum manufacturing condition. • -- To indicate what controls need to be adjusted , and potentially to even shut down the line. • -- Statistics could provide same result but with huge data.

  32. Existing Applications(3/4) • 5. Word Recognizer • --Intel used single speaker on limited vocabulary. • 6. Blower Motor Checker • --Siemens used to check Blower motor noise is heater. • 7. Medical events

  33. Existing Applications(4/4) • 8. US postal office for hand written • 9. Airline marketing tactician.

  34. Possible Applications(1/6) • 1. Biological • --Learning more about the brain and other systems • --Modeling retina , cochlea • 2. Environmental • --Analyzing trends and patterns • --Forecasting weather

  35. Possible Applications(2/6) • 3. Business • --Evaluating probability of oil in geological formations • --Identifying corporate candidates for special positions • --Mining corporate databases • --Optimizing airline seating and fee schedules • --Recognizing handwritten characters, such as Kanji

  36. Possible Applications(3/6) • 4. Financial • --Assessing credit risk • --Identifying forgeries • --Interpreting handwritten forms • --Rating investments and analyzing portfolios

  37. Possible Applications(4/6) • 5. Manufacturing • --Automating robots and control system (with machine vision and sensors for pressure. temperature, gas, etc.) • --Controlling production line processes • --Inspecting for quality • --Selecting parts on an assembly line

  38. Possible Applications(5/6) • 6. Medical • --Analyzing speech in hearing aids for the profoundly deaf • --Diagnosing/prescribing treatments from symptoms • --Monitoring surgery • --Predicting adverse drug reactions • --Reading X-rays • --Understanding cause of epileptic seizures

  39. Possible Applications(6/6) • 7. Military • --Classifying radar signals • --Creating smart weapons • --Doing reconnaissance • --Optimizing use of scarce resources • --Recognizing and tracking targets

  40. Experiment I • to understand a sentence are character a time is much larger than one word a time • conventional computer processes its input one of a time, working sequentially • our eyes look at the whole sentence • vowels are missing • three different groupings

  41. Experiment II(1/2) • Toss a chalk to another one • -- it is hard in dynamics • -- estimate the speed , the trajectory, the weight • -- in real time • -- computer must be faster

  42. Experiment II(2/2) • But • -- our brain is lower than computer • -- our brain still better than computer • Why? •  parallel processing

  43. Other Names for Artificial Neural Networks • Parallel/distributed processing models • Connectivist/connectionism models • adaptive systems • self-organizing systems • Neurocomputing • Neuromorphic systems • Self-learning systems

  44. Who Are the Key Players? (1/2) • 1. Medical and theoretical neurobiologists • --Neurophysiology, drug chemistry , molecular biology • 2. Computer and information scientists • --Information theory • 3. Adaptive control theorists/psychologists • --Merging learning and control theory

  45. Who Are the Key Players? (2/2) • 4. Adaptive systems • -- researchers/biologists • --Self-organization of living species • 5. AI researchers • --Machine learning mechanisms

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