Introduction to Artificial Neural Networks and its Applications

an introduction to

Artificial Neural Networks
and its applications
- Dr. Rajaram Kudli

Partners in Co-Creating Success

Process, Data and Domain driven Business Decision Life Cycle

Introduction to Artificial Neural Networks 1 www.compegence.com

Intelligent Systems

• What is Intelligence?

– The capacity for understanding or
the ability to perceive and
comprehend meaning - Cognition

– System or method able to modify
its action in the light of ongoing
events - Adaption


ABCs of Intelligence

• AI - Artificial Intelligence
– a branch of Computer Science concerned with the problems of
reasoning, knowledge representation, planning, learning, natural
language processing, communication, perception etc., thorough the
approaches of statistical methods, computational intelligence and
symbolic computation, aimed at engineering Intelligent Machines
• BI - Business Intelligence
– a technology enabled discipline comprising of functions viz., reporting,
online analytical processing, data mining, business performance
management, benchmarking, predictive & prescriptive analytics etc. that
enable an enterprise to discover actionable insights from all its data
• CI - Computational Intelligence
– a set of nature-inspired computational methodologies and approaches
to address complex real-world problems to which traditional
approaches, viz., ab-initio modeling or explicit statistical modeling, are
ineffective or infeasible

Computational Intelligence - Paradigms

• Artificial Neural Networks
– Human-Like Information Processing

• Fuzzy Logic
– Human-Understandable Reasoning

• Genetic Algorithms
– Human-Like Evolution

• Chaos Theory
– Humanity-Like Complex Behavior


Human Understandable Vs Human-Like

• Human Understandable • Human-Like
– Artificial Intelligence, – Artificial Neural
Fuzzy Logic and Genetic Networks; Chaos
Algorithms Computing
– Synthetic, rule-based – Natural, abstract
logical models models
– Easier to explain the – Harder to extract
knowledge & method of meaning from the
solution values
– Easier to gain – Harder to gain
acceptance acceptance



• Computational models inspired by the human brain
– Massively parallel, distributed system, made up of simple
processing units., neurons
– Synaptic connection strengths among neurons are used to
store the acquired knowledge.
– Knowledge is acquired by the network from its
environment through a learning process


Model Complexity

NNET
HIGH
ARIMA
AAR
MED
AR
Computational
Complexity
HWM
CM
HLES
LOW EMA
WMA
SMA

LOW MED HIGH
Model Complexity
(Forecasting Application)
Information Complexity

Non-regression Models Regression Models

Applicability – Where & Why ?

• Where? • Why?
– Where data is noisy, – Ability to solve data-
complex, imprecise, and intensive problems
hi-dimensional – Adaptation
– Where a clearly stated – Parallel Distributed
mathematical solution or Representation &
algorithm doesn’t exist Processing
– Where an explanation of – Fault tolerance
the decision is not – Nonlinearity
required
– Scalability
– Universality


Example – CPG-Retail Sales Forecasting

• An Intelligent Forecasting System that evaluates 10 classical
forecasting models including Neural Networks, and gives best
forecast acceptable to qualitative expectations of a human expert
ACTUAL SF_BEST

700

Validation Forecast
600

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a

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NNET 66%
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AAR 18%
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a s oa u ni y

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Week End Date


Example – Supervised Learning

• ALVINN, Autonomous Land Vehicle In a Neural Network, is a
perception system which learns to control the NAVLAB
vehicles by watching a person drive.


Example– Unsupervised Learning

• WEBSOM is a method for automatically organizing collections
of text documents and for preparing visual maps of them to
facilitate the mining and retrieval of information.


Example – Associative Memory

• Image Storage & Reconstruction by Hopfield network trained
on the sample images and then presented with either a noisy
cue or a partial cue.

Original, Stored Degraded Cue Reconstructed


Example - Controls

• HOAP, or Humanoid for Open Architecture Platform, represents
a fundamentally different approach to creating humanoid
robots, in harnessing the power of a neural network to tackle
movements and other tasks.


Application Areas in Engineering

• Aerospace: High performance aircraft autopilots, flight path simulations, aircraft
control systems, autopilot enhancements, aircraft component simulations, aircraft
component fault detectors
• Automotive: Automobile automatic guidance systems, warranty activity analyzers
• Electronics: Code sequence prediction, integrated circuit chip layout, process control,
chip failure analysis, machine vision, voice synthesis, nonlinear modeling
• Mechanical: Condition monitoring, Systems modeling and control
• Manufacturing: Manufacturing process control, product design and analysis, process
and machine diagnosis, visual quality inspection systems, beer testing, welding quality
analysis, paper quality prediction, computer chip quality analysis, analysis of grinding
operations, chemical product design analysis, machine maintenance analysis, project
bidding, planning and management, dynamic modeling of chemical process systems
• Robotics: Trajectory control, forklift robot, manipulator controllers, vision systems
• Telecommunications: Data compression, signal processing, pattern recognition: Face,
Objects, Fingerprints, Speech recognition; automated information services, real-time
translation of spoken language, customer payment processing systems, Equalisers,
Network Design, Management, Routing and Control, ATM Network Control, Fault
Management, Network Monitoring


Application Areas in Business

• Business Analytics: Market Research, Market Structure, Market Mix, Customer
behavior modeling, Propensity modeling for Purchase, Renewals, Default, Attrition,
Fraud, Market & Customer Segmentation
• Banking: Credit/Loan application evaluators, Fraud and Risk evaluation, Credit card
attrition, Delinquency
• Financial: Real estate appraisal, loan advisor, mortgage screening, corporate bond
rating, credit line use analysis, portfolio trading program, corporate financial analysis,
currency price prediction
• Education: Modeling Students’ performance, Personality Profiling, Diagnostics of a
modern state, analysis, and forecasting of dynamics of a system of education
• Defense: Weapon steering, target tracking, object discrimination, facial recognition,
new kinds of sensors, sonar, radar and image signal processing including data
compression, feature extraction and noise suppression, signal/image identification;
Counter-terrorism
• Medical: Breast cancer cell analysis, EEG and ECG analysis, prosthesis design,
optimization of transplant times, hospital expense reduction, hospital quality
improvement, emergency room test advisement
• Securities: Market analysis, automatic bond rating, stock trading advisory systems
• Transportation: Truck brake diagnosis systems, vehicle scheduling, routing systems


#1 - Predicting Stock Prices

• Walkrich Investment Advisors used Neural Networks to
produce an investment tool WRRAT based loosely on Warren
Buffett's ideas to predict stock prices, and determine which
stocks are trading below their market value. The results from
January 1995 to January 1996 showed that a Portfolio of
WRRAT's most under-priced shares saw an average advance of
33%.


#2 - Predicting S&P 500 Index

• LBS Capital Management used a neural network software to
predict the S&P 500 index. The company uses an expert
system to provide instructions to the neural network, which
then processes the data accordingly. When tested with
hundreds of previous days data the neural network LBS
trained predicts the S&P 500 with an accuracy of about 95%.


#3 – Predicting Currencies

• O'Sullivan Investments successfully used many neural
networks in order to advise them of market trends. Mr James
O'Sullivan produced an article Neural Nets: A Practical Primer,
AI In Finance, 1994 outlined some of the networks used. One
of the most important factors in producing a successful net is
to ask the right kind of question. Rather than simply ask what
the projected price of a currency might be, he asks at what
price the market is likely to take off in one direction or the
other etc.


#4 – Predicting Natural Gas Price

• Northern Natural Gas is a regulated wholesaler of natural gas.
They must develop and file a rate for the gas they sell based
on the average cost of the gas. By developing a neural
network that use factors such as the quarter of the year,
season, temperature last month etc. to predict the following
months oil price, the company was better able to plan rates.


#5 – Predicting Bonds

• G. R. Pugh & Company does consulting to predict the prices of
bonds of public utilities. The company used neural networks
to help forecast the following years corporate bond prices and
ratings of over 100 public utility companies. The network they
used compared very favourably to conventional mathematical
analysis. Whereas the network was able to predict a utilities
rating (A, B, C) with 95% accuracy, conventional mathematical
analysis was only effective 85% of the time. The only
difficulties encountered by the network were associated with
companies experiencing particularly unusual problems that
were not incorporated into the networks inputs.


#6 – Direct Mail Marketing

• Microsoft used neural networks to maximise the effectiveness
of their marketing campaign. Each year the company sent out
mail to its registered customers. Most of this mail offered
upgrades or new software but the response rate was rather
low. The company used a neural network that was fed various
variables such as how recently they registered, how many
products they have bought etc. to target users more
effectively. The results showed an average mailing lead to a
35% cost savings.


#7 – Credit Scoring

• Research conducted by Dr Herbert Jensen PhD demonstrated
that "building a neural network capable of analysing the credit
worthiness of loan applicants is quite practical and can be
done quite easily". The neural network was trained on no
more than 100 loan applications to process application data
such as occupation, years with employer etc. Despite the
relatively small training set the network achieved a 75-80%
success rate. This compared well with more traditional scoring
methods that resulted in about a 75% success rate.


#8 – Real Estate Appraisal

• Several neural networks have been used to predict the sale
prices of homes in order to help appraisers assess, sellers
estimate asking prices, and home owners decide on
improvements. Richard Borst successfully trained a neural
network to appraise real estate in the New York area. His
network incorporated almost 20 variables including the
square feet of living area, age, etc. He used over 200 sales
records from 1988 and 1989 to train the network with about
90% accuracy.


Summary

• ANN can solve the direct (prediction) and inverse
(control) problem easy and fast in spite of
incompleteness of data

• ANN can solve problems of higher complexity of
modeling, recognition, predictions, and control in
engineering & business, better than traditional solutions

• ANN paradigms provide powerful approaches to the
problem domains with high contact of theory,
simulation, experiment, data and human expertise


ANN Paradigms – Theory & Practice

• Feed Forward Networks (FFNN)
– Multi-Layer Perceptrons (MLP)

• Competitive Learning Networks (CLNN)
– Self-Organizing Maps (SOM)

• Recurrent Neural Networks (RNN)
– Hopfield Networks (HNN)


Process, Data and Domain driven Information Excellence

ABOUT COMPEGENCE


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Usage
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Partners in Co-Creating Success

Process, Data and Domain driven Information Excellence

Process, Data and Domain driven Business Decision Life Cycle

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Introduction to Artificial Neural Networks and its Applications

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