Transcript Neural Computing

Introduction to Neural
Networks And Their
Applications
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Table of Contents
I. Introduction of Neural Networks
II. Application of Neural Networks
III. Theory of Neural Networks
IV. A Neural Network Demo
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What is neural networks ?
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http://www.youtube.com/watch?v
=DG5UyRBQD4&feature=rellist&playnext
=1&list=PL4FA5D71B0BA92C1C
I. Introduction of Neural Networks
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It is simulation of human brain
It is the most well known artificial
intelligence techniques
We are using them: voice recognition
system, reading hand writes, door rocks
et al.
It is a called black box
It is a simulator for human brain
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Neural Networks simulate human brain
Learning in Human Brain
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Neural Networks As Simulator For Human
Brain
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Neurons
Connection Between Neurons
Processing Elements or Nodes
Weights
II. Applications of Neural Networks
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Prediction of Outcomes
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Patterns Detection in Data
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Classification
Business ANN Applications -1
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Accounting
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Finance
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Identify tax fraud
Enhance auditing by finding irregularities
Signatures and bank note verifications
Foreign exchange rate forecasting
Bankruptcy prediction
Customer credit scoring
Credit card approval and fraud detection*
Stock and commodity selection and trading
Forecasting economic turning points
Pricing initial public offerings*
Loan approvals
Business ANN Applications -2
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Human Resources
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Management
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Consumer spending pattern classification
Sales forecasts
Targeted marketing, …
Operations
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Corporate merger prediction
Country risk rating
Marketing
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Predicting employees’ performance and behavior
Determining personnel resource requirements
Vehicle routing
Production/job scheduling, …
III. Theory of Neural Networks
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Neural Computing is a problem solving
methodology that attempts to mimic
how human brain functions
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Artificial Neural Networks (ANN)
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Machine Learning/Artificial Intelligence
The Biological Analogy
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Neurons: brain cells
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Nucleus (at the center)
Dendrites provide inputs
Axons send outputs
Synapses increase or
decrease connection
strength and cause
excitation or inhibition of
subsequent neurons
Artificial Neural Networks (ANN)
Biological
Soma
Dendrites
Axon
Synapse
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<->
<->
<->
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Artificial
Node
Input
Output
Weight
Three Interconnected
Artificial Neurons
Basic structure of Neural Networks
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Network Structure : Layers, Nodes and Weights
Input Layer
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Hidden Layer
Output Layer
ANN Fundamentals
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ANN Fundamentals: how informatio
is processed in ANN
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Processing Information by the Network
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Inputs
Outputs
Weights
Summation Function
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Figure 15.5
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Learning in NN(Neural Network) is
finding the best numeric values (X),
representing input (4) and output(8)
relationship ( ex: 4 * X = 8 )
*Try with x= 1, x= 2, x=3, …… When x=4, it solve the problem.
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Compute outputs
Compare outputs with
desired targets
Adjust the weights
and repeat the
process
Neural Network Architecture
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There are several ANN architectures
:feed forward, recurrent, Hopfield et al.
Neural Network Architecture
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Feed forward Neural Network
: Multi Layer Perceptron, - Two, Three, sometimes
Four or Five Layers, But normally 3 layers are
common structure.
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How a Network Learns
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Step function evaluates the summation of
input values
Calculating outputs
Measure the error (delta) between outputs and
desired values
 Update weights, reinforcing correct results
At any step in the process for a neuron, j, we get
Delta(Error) = Zj - Yj
where Z and Y are the desired and actual outputs,
respectively
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Backpropagation
Initialize the weights
Read the input
vector
Generate the output
Compute the error
Error = Output –
Desired output
Change the weights
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Drawbacks:
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A large network can take a very long time to train
May not converge
Training A Neural Networks
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Neural Networks learn from data
Learning is finding the best weights
values which represent the input and
output relationship in Neural Networks
(ex: 4*X= 8)-> finding the value for X
training data set and test data set
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Collect data and separate it into
 Training set (50%), Testing set
 Training set (60%), Testing set
 Training set (70%), Testing set
 Training set (80%), Testing set
 Training set (90%), Testing set
(50%)
(40%)
(30%)
(20%)
(10%)
Use training data set to build model
Use test data set to validate the trained network
Prediction with New Data
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If the Neural Network's performance in
test is good , it can be used to predict
outcome of new unseen data
If the performance with test is not
good, you should collect more data,
add more input variables
How does Neural Network work
for prediction?
Terms in Neural
Networks
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Demo – How does Neural
Network work for prediction?
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ANN Development Tools
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E-Miner
Clementine
NeuroSolutions
Statistica Neural
Network Toolkit
Braincel (Excel Add-in)
NeuralWorks
Brainmaker
PathFinder
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Trajan Neural Network
Simulator
NeuroShell Easy
SPSS Neural Connector
NeuroWare
Why use Neural Networks in
Prediction? - major benefits of
Neural Networks
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Benefits of ANN
Advantages:
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Non-linear model leads to better performance
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It works generally good when data size is small
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It works generally good when there are noises in
data
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It works generally good when there are missing in
data (incomplete data set)
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Fast decision making
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Diverse Applications:
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Pattern recognition
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Character, speech and visual recognition
Limitations of ANN
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Black box that is hardly understood by
human
Lack of explanation capabilities
Training time can be excessive and
tedious
IV. A Neural Networks Demo
How do neural networks learn?
: trials and errors
http://www.youtube.com/watch?v=0
Str0Rdkxxo
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