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Neural Nets Applications Introduction

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Neural Nets Applications

Introduction

Outline(1/2)1. What is a Neural Network?2. Benefit of Neural Networks3. Structural Levels of Organization

in the Brain4. Models of a Neuron5. Network Architectures6. Artificial Intelligence and Neural

Networks

Outline(2/2)

7. Existing Applications

8. Possible Applications

9. Experiment I

10. Experiment II

11. Other names for Neural Networks

12. Who are the key player?

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.

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.

What is a Neural Networks (3/5)

It resembles the brain in two respects:1. Knowledge is acquired by the network

through a learning process.

2. Interneuron connection strengths known as synaptic weight are used to store the knowledge.

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

What is a Neural Networks (5/5)

Benefits of Neural Networks (1/2)

1. Nonlinearity

2. Input-Output Mapping

3. Adaptivity

4. Evidential Response

5. Contextual Information

Benefits of Neural Networks (2/2)

6. Fault Tolerance

7. Implementability

8. Uniformity of Analysis and Design

9. Neurobiological Analogy

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

2. Figure 1.3

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

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

Models of a Neuron (1/6)1. Figure 1.42. 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.

Models of a Neuron (2/6)

Models of a Neuron (3/6)3. Mathematical terms:

where:

xj: input signals

wkj: synaptic weights

uk: linear combiner output

θk:: thresholdf() : activation function

yk: output signal

)(

1

kkk

p

jjkjk

ufy

and

xwu

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

a. Threshold function

Models of a Neuron (5/6)4. Types of activation function:

b. Piecewise-linear function

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

c. Sigmoid Function

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

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

connected

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

connected

Network Architecture (4/5)3. Recurrent networks

Network Architecture (5/5)4. Lattice Structures

Artificial Intelligence and Neural Networks (1/5)

AI system

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.

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.

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

Artificial Intelligence and Neural Networks (5/5)

Existing Applications(1/4)

1. Long distance echo adaptive fitter adaptive noise canceling

-- ADALINE

2. Mortgage risk evaluator3. Bomb sniffer

-- SNOOPE -- JFK airport

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.

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

Existing Applications(4/4)

8. US postal office for hand written

9. Airline marketing tactician.

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

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

Possible Applications(3/6)4. Financial

--Assessing credit risk

--Identifying forgeries

--Interpreting handwritten forms

--Rating investments and analyzing portfolios

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

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

Possible Applications(6/6)7. Military

--Classifying radar signals

--Creating smart weapons

--Doing reconnaissance

--Optimizing use of scarce resources

--Recognizing and tracking targets

Experiment I 1. to understand a sentence are

character a time is much larger than one word a time

2. conventional computer processes its input one of a time, working sequentially

3. our eyes look at the whole sentence 4. vowels are missing5. three different groupings

Experiment II(1/2)1. 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

Experiment II(2/2)But

-- our brain is lower than computer

-- our brain still better than computer

Why?

parallel processing

Other Names for Artificial Neural Networks

Parallel/distributed processing modelsConnectivist/connectionism modelsadaptive systemsself-organizing systemsNeurocomputingNeuromorphic systemsSelf-learning systems

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

Who Are the Key Players? (2/2)

4. Adaptive systems

-- researchers/biologists

--Self-organization of living species

5. AI researchers

--Machine learning mechanisms