neural nets applications introduction. outline(1/2) 1. what is a neural network? 2. benefit of...
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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
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
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 (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
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
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
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