Download Artificial Neural Networks

Introduction & Fundamentals
• Part I: Introduction
• Part II: Fundamental Concepts
• Part III: Classification Lab
Part I: Introduction
What Is The Problem?
Our world is full of data. After collection and
organization, data, if we are lucky, becomes
information. In today's interconnected world,
information exists in electronic form that can be
stored and transmitted instantly. Challenge is to
understand, integrate, and apply information to
generate useful knowledge (“actionable intelligence”)
Are we drowning in data/information but starved for
knowledge??
Data, Data Everywhere…….
How do we extract knowledge from noisy mass of
data ?
Every source of data, from process and product
manufacturing to medical research to activity in
financial markets to patient examinations, to the billions
of consumer and business purchase transactions that
occur every day, is influenced by “other data” from the
surrounding environment. Our world is a noisy and
messy source of data - virtually nothing is known with
certainty. Knowledge is based on data analysis that
accommodates uncertainty.
Empirical Models that Learn
What Is The Solution?
Interpretation requires data acquisition, cleaning
(preparing the data for analysis), analysis, and
presentation in a way that permits knowledgeable
decision making and action. Key is to extract
information about data from relationships buried
within the data itself.
Tools and Technology
Human brain is most powerful pattern recognition
engine ever invented, however, it is not very good at
serially processing huge quantities of discrete data.
Enter New Breed of Processor:
Artificial Neural Networks
• Instead of programming computational system to do
specific tasks, teach system how to perform task
• To do this, generate Artificial Intelligence System- AI
• Empirical model which can rapidly and accurately
find the patterns buried in data that reflect useful
knowledge
• One case of these AI models is neural networks
• AI systems must be adaptive – able to learn from
data on a continuous basis
Artificial Intelligence
• Artificial Intelligence techniques such as
Neural networks, genetic algorithms and fuzzy
logic are among the most powerful tools
available for detecting and describing subtle
relationships in massive amounts of seemingly
unrelated data.
• Neural networks can learn and are actually
taught instead of being programmed.
• Teaching mode can be supervised or
unsupervised
• Neural Networks learn in the presence of noise
Question: What are Artificial Neural Networks?
Answer:
Output Layer
………
Hidden Layer 2
………
Hidden Layer 1
………
Input Buffer
………
Biological Basis of ANNs
Biological Basis of ANNs
• Animals exhibit intelligence……………….
• Biological neural networks…………..
• Human beings can benefit from simulation of biological
neural networks on computers. These are Artificial Neural
Networks (ANN)
• Artificial Neural Networks (ANN) are……….
• ANN’s represent an attempt to simulate……
• ANNs have many names: connectionist
systems, neural nets, neurocomputers, parallel
distributed processing systems, machine
learning algorithms, etc
• Each neuron is linked to its neighbors with
varying coefficients of connectivity that represent
the strengths of these connections
• Learning…………..
What are Neural Networks Used For?
2 Basic Types of Learning
Neural Nets
Supervised
Learning
Unsupervised
Learning
Types of Problems
• Mathematical Modeling (Function
Approximation)
• Classification
• Clustering
• Forecasting
• Vector Quantization
• Pattern Association
• Control
• Optimization
Mathematical Modeling
(Function Approximation)
• Modeling – often mathematical relationship between two sets of data
unknown analytically
• No closed form expression available
• Empirical data available defining output parameters for each input
• Data is often noisy
• Need to construct a mathematical model that correctly generates
outputs from inputs (See fig 1.22 on page 29)
• Approx carried out using ………………………….
• Network learns ………………………………………….
• Trained Neural Net can be substituted for ………………………….
• Fast computation, reasonable approximation
Classification
• Assignment of objects to specific class
• Given a database of objects and classes
of those objects
• Deduce ……………………………
• Create a classifier that will ………………..
Clustering
• Grouping together objects similar to one another
• Usually based on some “distance” measurement
in object parameter space
• Objects and distance relationships available
• No prior info on classes or groupings
• Objects clustered based on ………………..
• Clustering may precede ………………………
• Similar to statistical k-nearest neighbor
clustering method
Forecasting
• Prediction of future events based on history
• Laws underlying behavior of system sometimes
hidden; too many related variables to handle
• Trends and regularities often masked by noise
• Prediction system must be able to ……………….
• Time series forecasting – special case of ……….
• Weather, Stock market indices, machine
performance
Vector Quantization
• Kohonen classifier – most well known
• Object space divided into several connected
regions
• Objects classified based on proximity to regions
• Closest region or node is “winner”
• Form of compression of high dimensional input
space
• Successfully used in many geological and
environmental classification problems where
input object characteristics often unknown
Pattern Association
• Auto-associative systems useful when incoming
data is a corrupted version of actual object e.g.
face, handwriting
• Corrupt input sample should trigger ……………
• Require a response which …………………
• May require several iterations of repeated
modification of input
• Will be discussed under ……………………..
Control
• Manufacturing, Robotic and Industrial machines
have complex relationships between input and
output variables
• Output variables define state of machine
• Input variables define machine parameters
determined by operation conditions, time and
human input
• System may be static or dynamic
• Need to map inputs to outputs for stable smooth
operation
• Examples include chemical plants, truck backup,
robot control
Optimization
• Requirement to improve system performance or
costs subject to constraints
• Maximize or Minimize …………………….
• “Terrain” of objective function typically very
…………………………………..
• Large number of …………….. affecting
objective function (high …………… of problem)
• Design variables often subject to ……………….
• Lots of local ………………………..
• Neural nets can be used to find global optima
(Ch 7)
Now for some Practical
Applications !
Neural Network Applications
•
•
•
•
•
•
•
Neural networks have performed
successfully where other methods have
not, predicting system behavior,
recognizing and matching complicated,
vague, or incomplete data patterns.
Apply ANNs to pattern recognition,
interpretation, prediction, diagnosis,
planning, monitoring, debugging,
repair, instruction, control
GOTCHA!
Biomedical Signal Processing
Biometric Identification
System Reliability
Business
Spiral Inductor Modeling
Target Tracking
• Common use for neural networks is to
project what will most likely happen demand prediction. Can help in setting
……………………. For example, hospital
emergency rooms, communications
systems, power distribution, consumer
goods manufacture and storage, …………
• Extremely successful in categorization,
pattern recognition. System classifies object
under investigation (e.g. an illness, a
pattern, a picture, a chemical compound, a
word, the financial profile of a customer) into
one of numerous possible categories. This
triggers …………………………………
GOTCHA!
• GOTCHA………………………….
• Current surveillance and reconnaissance systems (S&R) structured to
observe huge areas, attempt to detect movement of hostile forces.
• “Forensic” approach: …………………………………….
•
• USAF Command and Control Battlelab (C2B): use past S&R imagery
to locate an explosion, run data back in time to identify the vehicle (or
object) which carried munitions, “lock” onto vehicle & backtrack to
locate significant portions of path – assembly areas, passenger pickup, arming site, and any other spot with intelligence value
• Technology: imagery, net-centric gathering & sharing of data, target
identification, …………………………..
• Collaboration of information ………………………..
Biomedical Signal Processing
Biometric Identification
• “Instant Physician” developed
using neural net
• Net presented with a set of
symptoms, medical records
• Output is best diagnosis and
treatment
• Finger prints never change. Bifurcations or “Minutae”
………………………………………
• Minutiae-based techniques find minutiae points and map
their relative placement on the finger
• Large volumes of fingerprints are collected and stored
everyday in a wide range of applications including
forensics, access control, and driver license registration
• Automatic recognition of people based on fingerprints
requires ………………………….
•
FBI database contains 70 million fingerprints!
System Control & Reliability
• Backing Up a truck to a loading dock is
a difficult problem for a novice, easy for
an experienced driver
• Very difficult problem mathematically
• Can train a neural net to
…………………….
• Automobile airbags can do serious
damage …………………………
• Accelerometer MEMS are …………….
• System reliability continuously assessed &
failure pre-empted by correct interpretation
of data from accelerometers
Business
• Mortgage Risk Assessment – reduces
delinquency rates
• Inputs include years of employment, #
of dependents, property info, income,
loan-to-value-ratio
• Output is ……………………….
• Prediction of of behavior of stock market
indices
• Requires knowledge of ……………..
• Time series forecasting
• Short and long term predictions
Spiral Inductor Modeling
• In today’s portable wireless communications market, demand is for
low cost, low power dissipation, high frequency IC building blocks that
incorporate spiral inductors on the silicon substrate
• Challenge: ………………………
• Empirical models widely reported based on actual measurements but
non-predictive and do not permit re-design of inductor layout
• Neural network approach serves as basis for ………………… …….
and permits ………………………. from post-optimization inductor
circuit-level parameters
• Ilumoka& Park, Proc. SSST 04, Georgia Tech, Atlanta GA, March
2004
Automatic Target Tracking & Recognition
• Algorithms for automated tracking
& recognition of targets have
recently been identified by the
National Critical Technologies
Panel as of critical importance to
mission of the Department of
Defense.
• Automated target recognition,
localization, and tracking in the
presence of ……………. is an
important signal processing
problem
• Algorithms have been developed
for …………………………… such
as those that occur during active
jamming, non-cooperative
maneuvering & complex battlefield
scenarios of the future
MUTUAL FUNDS: NEURAL NETWORKS versus
REGRESSION ANALYSIS
• For neural networks to be successful, they must outperform
methods currently being used in the marketplace.
• Mutual funds are basically ……………………………………. ….
Mutual funds have become a major force on Wall Street over the
past few years. They function much like an individual security and
their prices should reflect all public information. Relationships
between …………………………………………………. are very hard
to forecast. For years, regression analysis has been a popular tool
investors have used to forecast …………………… of mutual funds.
• Investors know that neural networks might be able to pinpoint these
relationships better than old methods.
• Predictions made for Net Asset Value using 15 economic variables
as inputs showed that neural networks were 40% better as tools for
forecasting: …………………………….(difference between actual
and forecasted NAV) was ………. for neural nets as compared to
…….. for regression.
• Important reason for superior performance of neural networks is its
………….. It was able to look at all aspects of relationships, whereas
regression analysis was ………………………………………………
• .
Historical Perspective
Origin of ANNs is neurobiological research in the
early 20th century
Several fronts of attack:
• Neurobiologists: How do nerve cells behave
when stimulated by an electric current?
• Psychologists: How is learning accomplished
by animals?
• Mathematicians: How can we apply gradient
descent to neuron learning?
• McCulloch & Pitts – 1st math model of neuron
• Learning rules devp by Hebb (1945),
Rosenblatt (1958), Widrow-Hoff (1961)
• Several Limitations encountered
• See pages 5-7 for chronological history
Neurons
• Biological neurons are ………………………. receiving & sending
signals across synapses to other neurons via tree-like dendrites at
both ends (see fig 1.3, page 8)
• Artificial neurons are ……………………….. (fig 1.4, page 10) in
which inputs are weighted and summed to produce a weighted sum
output net
• Activation function f is ……………………………… f(net)
corresponding to firing frequency of the biological neuron
• Several different activations are possible including ………………
• Although neural networks have great potential, there is still a long
way to go. Complex neural networks have less than the brain power
of a ………………. (100,000 neurons). Human brains contain about
…………………….. neurons.
• Neural network software sales annually exceed $2 billion because
they offer ………………………………..
Part II: Fundamental Concepts
_ a _a _ _ _ m
What is a conceptual framework that permits
investigation of phenomena in a field of enquiry ?
Answer
……………………..
Important ANN Parameters
1. Architecture (or Topology)
2. Learning Rule
3. Paradigm: Combination of Architecture & Learning Rule – complete
neural network model - emulates ………………………….
………………..
………………..
PARADIGM
Architecture
MLP Feedforward
• What is architecture? ……………………..
• Single node, single layer insufficient for
practical problems; require multiple nodes
connected by excitatory (positive) or
inhibitory (negative) weights
• 3 types of nodes: ………..(receive external
inputs), ………. (generate external
outputs) , ……….. (no interaction with
external environment)
• Nodes often partitioned into layers
(layered nets); intra-layer connections may
be prohibited (acyclic) – see fig 1.13, 1.14
page 19
• Feedforward nets – ……………………….
LVQ
Hopfield Net
Hamming
BAM
• Recurrent nets – ……………………………
ART
Modular Net
Backpropagation
Learning Rule
• What is a learning rule or algorithm ?
• Learning is process by which neural
net adapts itself to stimulus in order to
produce a desired response
• Learning rule is …………………….
• Just as individuals learn differently,
neural network have different learning
rules
• Learning may be Supervised or
Unsupervised
• Supervised learning requires that
when the input stimuli are applied, the
desired output is known a priori
Competitive
Correlation
…………….
………………..
Paradigms
Examples of Paradigms
• Adaptive Resonance Theory (ART) (………………………………)
• Modular Neural Networks MNN (……………………………………)
• Learning Vector Quantization (……………………)
Modular Neural Network Paradigm successfully applied to
Spiral Inductor Modeling
Backpropagation
Modular Architecture
Course Objective
To understand, successfully apply
and evaluate neural network
paradigms for problems in science,
engineering and business
Supervised Learning
Will begin with supervised learning. Procedure is:
Select Neural Net …………… & …………………..
Present………. to neural net
Supply ……………………
Train neural net to learn ………………………………
Test or Verify that network has learned and can ……………. well
………………….. network
Biological Neural Net Example :
Human Eye
retina
fovea
rods
•
•
•
•
•
•
•
•
•
•
•
cones
lens
optic
Eye is ………………. of brain – powerful bio-electrochemical computer
nerve
Light enters thru ……and focuses on …….. (similar to photographic film)
Retina is dense matrix of photoreceptors – ……………………………….
Rods – form ……… images in dim light 100X more sensitive than cones
Cones handle …………….., 4X faster than rods in response to light
Rods, Cones convert light to electric signals, total 130million, 6% cones
Highest conc of cones is in ……….., 1.5mm diameter, 2000 cones
Retinal neurons arranged in layers receive electric signals via synapses
Pre-processing of image takes place at retinal level in neuron network
Signals arrive at …………….(1 million), axons of which form ………….
Optic nerve fibers terminate in lateral geniculate nucleus LGN in brain
Visual Pathways
Layers of retinal
neurons
cones
rods
Right
Optic Nerves
from L & R eye
to R & L LGN
Left
Questions on Biological Example
• Q1: What is basic building
block of nervous system?
• Q2: What are
basic parts of
neuron?
• Q3: If cones were
absent from retina,
how would color
pictures be
perceived?
• Q4:How can eye
be adjusted to
large differences
in light intensity?
(e.g. sun to star
is 10billion range
Part III: Classification Lab
Botanical Application Example:
Iris Flower Classification
Botanical Application Example:
Iris Flower Classification
• 3 species of Iris – SETOSA, VERSICOLOR, VIRGINICA
• Each flower has parts called PETALS & SEPALS
• Length and Width of sepal & petal can be used to
determine iris type
• Data collected on large number of iris flowers
• For example, in one flower petal length=6.7mm and
width=4.3mm also sepal length=22.4mm & sepal width
=62.4mm. Iris type was SETOSA
• Neural net will be trained to determine specie of iris for
given set of petal and sepal width and length
Iris training and testing data:
Sepal Length
Sepal Width
Petal Length
Petal Width
Iris Class
0.224
0.624
0.067
0.043
Setosa
0.749
0.502
0.627
0.541
Veracolor
0.557
0.541
0.847
1.000
Virginica
0.110
0.502
0.051
0.043
Setosa
0.722
0.459
0.663
0.584
Veracolor
0.776
0.416
0.831
0.831
Virginica
0.196
0.667
0.067
0.043
Setosa
0.612
0.333
0.612
0.584
Veracolor
0.612
0.416
0.812
0.875
Virginica
0.055
0.584
0.067
0.082
Setosa
0.557
0.541
0.627
0.624
Veracolor
0.165
0.208
0.592
0.667
Virginica
0.027
0.376
0.067
0.043
Setosa
0.639
0.376
0.612
0.498
Veracolor
0.667
0.208
0.812
0.710
Virginica
0.306
0.710
0.086
0.043
Setosa
0.196
0.000
0.424
0.376
Veracolor
0.612
0.502
0.694
0.792
Virginica
0.137
0.416
0.067
0.000
Setosa
Iris Flower Classification
• Since output is non-numeric, will use a 3bit binary code
to specify output
• 1 0 0 represents SETOSA
• 0 1 0 represents VERSICOLOR
• 0 0 1 represents VIRGINICA
• Columns 1-4 rep sepal L, W and petal L, W in mmX0.01
• Sample data below
0.224
0.749
0.557
0.11
0.722
0.776
0.196
0.624
0.502
0.541
0.502
0.459
0.416
0.667
0.067
0.627
0.847
0.051
0.663
0.831
0.067
0.043
0.541
1
0.043
0.584
0.831
0.043
1
0
0
1
0
0
1
0
1
0
0
1
0
0
0
0
1
0
0
1
0