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Chapter0. Overview of the Neural Network

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Chapter0. Overview of the Neural Network. Neural Network = Computational Structure of the Brain = Distributed, Adaptive, Nonlinear Learning Machine built from many Processing Elements - PowerPoint PPT Presentation
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1 Brain = Neural Network in Topology Chapter0. Overview of the Neural Network Neural Network = Computational Structure of the Brain = Distributed, Adaptiv e, Nonlinear Learning Machine built from many Processing Elements Synonyms : Artificial Neural Syste m, Artificial Neural Netwo rk, Neuromorphic System, Parallel Distributed Pr ocessing, Adaptive Network, Connn ectionism, Neurocompute Cell body
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1. Brain = Neural Network in Topology

Chapter0. Overview of the Neural NetworkNeural 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.

Cell body

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● Sensory Perception (Pattern Recognition) and motor control – Vision, audition, olfaction, touch, temperature sensing

● Learning By Examples – Non-algorithmic, Trainability, Self-Organization

● Planning and Reasoning

● 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)

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

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● 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.

Intelligence ?

3. Intelligent [Learning] Machines

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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

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(Ref. Eberhart, Chap. 1 of Computational Intelligence, IEEE Press, 95)

 Intelligence

CIBI

AI

SymbolicComp.

  Numeric Comp.

   Biology

MI = AI or CI

NN

FL

   EC

NFE

NE

NF

FE

CI

BI

Intelligence

Bezdek

4. Computational Intelligence and Soft Computing

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5. Neural Network Architecture

w1

dendrite

cell (soma)

axon

x1

wm

xm

wm+1xm+1

y

ftn activation :

1

1

m

jjj xwy

Activation function

Linear Combination

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(1) Feedforward Architecture

Input

Hidden Output

.... ....General

Layered(3-2-2)

Input Hidden Output

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(2) Feedback, Recurrent Architecture

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6 . Usage of the Neural Network – Function Approximation and Generalization

◆ Training

NN

NN

Teacher

Word

e-

+

Voice NN

Teacher

Object Image

e

Object Name

-

+

Amorphous

◆ Training

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◆ Apply to Different Tasks After Training

NN

Voice Word

Object Image Object Name

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Learning Mode (weights change) Performance Mode (weights fixed)

◆ Training – Function Approximation = Create Internal Representations Only through Examples

NeuralNetwork

x)(),( xwxy fF

y

x

y f(x)

f (x): Normally Unknown

7. Generalization By Nonlinear Interpolation

Digital Computer vs. Neural Computer

• Digital – recalculate even for same inputs

• Neural – can memorize and recall results of previous calculation [previous answers].

if w is fixed after training →

NN is a Model-Free EstimatorTraining Data

Discrete Samples

Underlying Function

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◆ Generalization

xy = F ( x , w * )

NN

Function Approximation

F ( x , w * )

x

F ( x , w )F ( x , w " )

F ( x , w ' )

f ( x ) ② ①

① ① Approximation ② Generalization

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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

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◆ 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 ( AIWARE 사의 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)

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Industrial App of CIIndustrial App of CIGE

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 & musi

c.

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9. NN Development Tools

◆ TypesGeneral Purpose Computer

1) S/W Simulation 2) S/W Simulation with H/W Accelerators3) S/W Simulation on a Parallel Computer

Special Purpose H/W1) Neurocomputing Workstations2) 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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