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Chapter0. Overview of the Neural Network

Chapter0. Overview of the Neural Network. Neural Network = Computational Structure of the Brain = Distributed, Adaptive, Nonlinear Learning Machine built from many Processing Elements

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Chapter0. Overview of the Neural Network

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  1. Chapter0. Overview of the Neural Network Neural Network = Computational Structure of the Brain = Distributed, Adaptive, Nonlinear Learning Machine built from many Processing Elements Synonyms : Artificial Neural System, Artificial Neural Network, Neuromorphic System, Parallel Distributed Processing, Adaptive Network, Connnectionism, Neurocomputer. 1. Brain = Neural Network in Topology Cell body

  2. ● Sensory Perception (Pattern Recognition) and motor control – Vision, audition, olfaction, touch, temperature sensing ● Planning and Reasoning ● Learning By Examples – Non-algorithmic, Trainability, Self-Organization ● Learning and Adaptation by Generalization ● Reflex and Intuition (Similar to Table Lookup) ● Processing Ill-defined (Unstructured, Inconsistent, Probabilistic, Noisy) Information – fault-tolerant, flexible, robust ● As Computer: Massively parallel and distributed : Algorithm + Architecture ● As Computer: Wetware (Netware) vs. Software/Hardware ● As Math: Nonlinear and Adaptive Modeling Scheme 2. Features of the Human Brain (Differences from Digital Computers)

  3. At birth, 10 11 neurons Innate, Tends to Decrease with Time. However, their interconnections evolve and new ones are created.

  4. 3. Intelligent [Learning] Machines Intelligence ? ●Webster = Ability to learn or understand or to deal with new or trying situations : REASON ●Jang = Humanlike Expertise, adapt and learn to do better in changing env. And explain its decisions/actions ●Constituent Tech. = Synergy of NN, FL, EC EC = Systematic random search = Biological genetics + Natural selection) ●CI = Any methodology involving computing exhibiting ability to learn (do better) with new situations by reasoning (generalization, discovery, association, abstracting); also explain how it reasons.

  5. History : Intelligence to Machines  Freedom to Mankind ! Industrial Revolution (Machine)  Information Technology Revolution  Artificial Brain (Intelligence) Revolution : Creative, human-friendly, autonomous (adaptive) Brain = Final Frontier = Neural Nets ●Left Brain (Logic) – Program (Symbolic, Software, Structured)  Machine Learning, Expert System : AI ●Right Brain (Intuition, Emotion) – Neural Network (Numeric, Hardware, Unstructured)  Cybernetics, Bionics

  6. Symbolic Comp. Intelligence NN Intelligence AI CI FL EC NE NF NFE Numeric Comp. Biology BI FE CI BI MI = AI or CI 4. Computational Intelligence and Soft Computing (Ref. Eberhart, Chap. 1 of Computational Intelligence, IEEE Press, 95) Bezdek

  7. S: Linear Combination Activation function 5. Neural Network Architecture

  8. (1) Feedforward Architecture Input General .... .... Hidden Output Layered(3-2-2) Output Hidden Input

  9. (2) Feedback, Recurrent Architecture

  10. NN NN Object Name Object Image e Teacher 6 . Usage of the Neural Network – Function Approximation and Generalization Amorphous ◆ Training ◆ Training NN Word Voice - - e + + Teacher

  11. NN ◆ Apply to Different Tasks After Training Voice Word Object Name Object Image

  12. x = @ y x w x F ( , ) f ( ) Neural Network y x y x f ( ) x f ( ): Normally Unknown 7. Generalization By Nonlinear Interpolation Learning Mode (weights change)  Performance Mode (weights fixed) ◆ Training – Function Approximation = Create Internal Representations Only through Examples if w is fixed after training → NN is a Model-Free Estimator Training Data • Digital Computer vs. Neural Computer • Digital – recalculate even for same inputs • Neural – can memorize and recall results of previous calculation [previous answers]. Discrete Samples Underlying Function

  13. NN Function Approximation x w F ( , ) x w " F ( , ) x y x w * = F ( , ) x w * F ( , ) x f ( ) x w ' F ( , ) x ◆ Generalization ① ② ② ① ① Approximation ② Generalization

  14. 8. Applications • 1) Pattern Recognition & Character Recognition, Document Search Face and Speech Recognition - Biometrics Computer Vision : Image Understanding, Object Recognition, PCB Inspection 2) Model Building from Experimental Data: Function Mapping, Regression, System Identification, Data Mining (Knowledge Discovery), Prediction 3) Image / Signal Processing and Communication 4) Optimization 5) Time Series Analysis and Financial Engineering 6) Medicine  - Patient Care and Clinical Decision Support, Biomedical Engineering, Bio-mimetics, Bio-informatics • 7) Robotics and Automation • ◆ Service Robots, Pet Robots, Surveillance Robots

  15. ◆ Process Control Kodak - Film Making Amoco – Oil Exploration ◆ Aircraft Control ◆ Automotive Control ◆ Machine Control / Maintenance ◆ Machine Health Monitoring / Diagnosis ◆ Diagnostics and Quality Control ◆ Power System Control ( Canada Vancouver Island Power) ◆ Chemical Product Design ( AIWARE사의 CAD/Chem ) ◆ Airline Luggage Inspection System ( -20 % cost + 50 % performance ) ◆ Active Vibration Cancellation • 8) Music Composition • 9) Neural Network Products in Korea: • Green Technology – Counterfeit Recognizer ( W 66B for 5 yrs) • Korea Axis – Speech Recognition Toy (2000. 12) • Slip Processing Machine for Banks using Handwritten Recognition (2000.12) • Speech Recognition Chip Product which is Robust to Noise Applying Human Auditory Model (2000. 7)

  16. Industrial App of CI GE Imagination at work Insurance underwriting, proactive maintenance Recom. Paper Web time-to-break prediction with Fuzzy+nn Equip Prognosis, anomally dete CI model = domain knowledge + field data Ford: CIS system DOD Air Force Res. Lab. Image Patterns below Clutter Tracking & detection below clutter NN appli – bioinfo, drug design, financial mkr pred, internet search engine, medical app, 30 commercial componemts, Future Dir. Drug design (big) nat lang under, search eng, high leve sensor fusion, sensor-web, neural & psycholinguistic study – working of mind, cog and emo, lang & music.

  17. 9. NN Development Tools • ◆ Types • General Purpose Computer • 1) S/W Simulation • 2) S/W Simulation with H/W Accelerators • 3) S/W Simulation on a Parallel Computer • Special Purpose H/W • 1) Neurocomputing Workstations • 2) Electronic - VLSI • 3) Optical - Laser Holography 10. Government Sponsored NN Research Worldwide 1990-2000 Decade of the Brain (US) 1990-2090 Century of the Brain (Japan) 1998-2007 Braintech 21(Korea) – Brain Research Promotion Act

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