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CHAPTER 16
Neural Computing Applications, and Advanced Artificial Intelligent
Systems and Applications
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Neural Computing Applications, and Advanced Artificial Intelligent
Systems and Applications
Several Real-World Applications of ANN Technology Advanced AI Systems
– Genetic Algorithms
– Fuzzy Logic
– Qualitative Reasoning
Integration (Hybrids)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Areas of ANN Applications:An Overview
Representative Business ANN Applications
Accounting Finance Human Resources Management Marketing Operations
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Accounting
Identify tax fraud Enhance auditing by finding irregularities
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Signatures and bank note verifications Mortgage underwriting Foreign exchange rate forecasting Country risk rating Bankruptcy prediction Customer credit scoring Credit card approval and fraud detection Stock and commodity selection and trading
Finance
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Credit card profitability Forecasting economic turning points Bond rating and trading Pricing initial public offerings Loan approvals Economic and financial forecasting Risk management
Finance 2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Predicting employees’ performance and behavior Determining personnel resource requirements
Human Resources
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Management
Corporate merger prediction Country risk rating
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Consumer spending pattern classification Customers’ characteristics Sales forecasts Data mining Airline fare management Direct mail optimization Targeted marketing
Marketing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Operations Airline crew scheduling Predicting airline seat demand Vehicle routing Assembly and packaged goods inspection Quality control Matching jobs to candidates Production/job scheduling Factory process control
Many More
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Credit Approval with Neural Networks
Increases loan processor productivity by 25 to 35% over other computerized tools
Also detects credit card fraud
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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The ANN Method
Data from the application and into a database
Preprocess applications manually
Neural network trained in advance with many good and bad risk cases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Neural Network Credit AuthorizerConstruction Process
Step 1: Collect data
Step 2: Separate data into training and test sets
Step 3: Transform data into network inputs
Step 4: Select, train, and test network
Step 5: Deploy developed network application
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Bankruptcy Prediction with Neural Networks
Concept Phase
Paradigm: Three-layer network, back-propagation
Training data: Small set of well-known financial ratios
Data available on bankruptcy outcomes
Supervised network
Training time not to be a problem
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Application Design
Five Input Nodes
X1: Working capital/total assets
X2: Retained earnings/total assets
X3: Earnings before interest and taxes/total assets
X4: Market value of equity/total debt
X5: Sales/total assets
Single Output Node: Final classification for each firm – Bankruptcy or – Nonbankruptcy
Development Tool: NeuroShell
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Development– Three-layer network with backpropagation (Figure 16.3)
– Continuous valued input
– Single output node: 0 = bankrupt, 1 = not bankrupt
Training– Data Set: 129 firms
– Training Set: 74 firms; 38 bankrupt, 36 not
– Ratios computed and stored in input files for:• The neural network
• A conventional discriminant analysis program
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Architecture of the Bankruptcy Prediction Neural Network
(Figure 16.3)
X4
X3
X5
X1
X2Bankrupt 0
Not bankrupt 1
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Parameters– Learning threshold– Learning rate– Momentum
Testing– Two Ways
• Test data set: 27 bankrupt firms, 28 nonbankrupt firms• Comparison with discriminant analysis
– The neural network correctly predicted:• 81.5 percent bankrupt cases • 82.1 percent nonbankrupt cases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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ANN did better predicting 22 out of the 27 actual cases
Discriminant analysis predicted only 16 correctly
Error Analysis– Five bankrupt firms misclassified by both methods
– Similar for nonbankrupt firms
Neural network at least as good as conventional
Accuracy of about 80 percent is usually acceptable for neural network applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Stock Market Prediction System with Modular Neural Networks
Accurate Stock Market Prediction - Complex Problem
Several Mathematical Models - Disappointing Results
Fujitsu and Nikko Securities: TOPIX Buying and Selling Prediction System
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Input: Several technical and economic indexes
Several modular neural networks relate past indexes, and buy/sell timing
Prediction system– Modular neural networks
– Very accurate
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Network Architecture(Figure 16.4)
Network Model: 3 layers, standard sigmoid function, continuous output [0, 1]
High-speed Supplementary Learning Algorithm
Training Data – Data Selection
– Training Data
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Preprocessing: Input Indexes - Converted into spatial patterns, preprocessed to regularize them
Moving Simulation Prediction Method (Figure 16.5)
Result of Simulations– Simulation for Buying and Selling Stocks
– Example (Figure 16.6)
– Excellent Profit
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Integrated ANNs and Expert Systems
1. Resource Requirements Advisor
– Advises users on database systems’ resource requirements
– Predicts the time and effort to finish a database project
– ES shell AUBREY and neural network tool NeuroShell
– ES supported data collection
– ANN used for data evaluation
– ES final analysis
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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2. Personnel Resource Requirements Advisor
– Project personnel resource requirements for maintaining networks or workstations at NASA
– Rule-based ES determines the final resource projections
– ANN provides project completion times for services requested(Figure 16.7)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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3. Diagnostic System for an Airline
– Singapore Airlines
– Assists technicians in diagnosing avionics equipment
– INSIDE (Inertial Navigation System Interactive Diagnostic Expert)
– Designed to reduce the diagnostic time(Figure 16.8)
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4. Manufacturing Product Liability
– United Technologies Carrier
– Two ES + ANN
– Patterns fed into multilayer feedforward ANN
– Integrated with a database into an Automatic Early Warning System (AEWS)
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5. Oil Refinery Production Scheduling and Environmental Control– Citgo Petroleum Corporation
– Lower costs
– Improved safety
– Higher product quality
– Higher yields
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Genetic Algorithms
Goal (evolutionary algorithms): Demonstrate self-organization and adaptation by exposure to the environment
System learns to adapt to changes. Example 1: Vector Game
– Random trial and error
– Genetic algorithm solution
Process (Figure 16.9) Example: the game of MasterMind
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Genetic Algorithm
Definition and Process Genetic algorithm: "an iterative procedure maintaining a
population of structures that are candidate solutions to specific domain challenges” (Grefenstette [1982])
Each candidate solution is called a chromosome
Chromosomes can copy themselves, mate, and mutate
Use specific genetic operators - reproduction, crossover and mutation
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Primary Operators of Most Genetic Algorithms
Reproduction
Crossover
Mutation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Genetic Algorithm Operators
1 0 1 0 1 1 1
1 1 0 0 0 1 1
Parent 1
Parent 2
1 0 1 0 0 1 1
1 1 0 0 1 1 0
Child 1
Child 2 Mutation
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GA Example: The Knapsack Problem
Item: 1 2 3 4 5 6 7 Benefit: 5 8 3 2 7 9 4 Weight: 7 8 4 10 4 6 4 Knapsack holds a maximum of 22 pounds Fill it to get the maximum benefit Solutions take the form of a string of 1’s Solution: 1 1 0 0 1 0 0 Means choose items 1, 2, 5. Weight = 21, Benefit = 20 Evolver solution in Figure 16.10
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Genetic AlgorithmsApplications and Software
Type of machine learning
Set of efficient, domain-independent search heuristics for a broad spectrum of applications
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Genetic Algorithm Application Areas
Dynamic process control Induction of rule optimization Discovering new connectivity topologies Simulating biological models of behavior and evolution Complex design of engineering structures Pattern recognition Scheduling Transportation Layout and circuit design Telecommunication Graph-based problems
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Business Applications
Channel 4 Television (England) to schedule commercials Driver scheduling in a public transportation system Jobshop scheduling Assignment of destinations to sources Trading stocks Productivity in whisky-making is increased
Often genetic algorithm hybrids with other AI methods
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Representative Commercial Packages
Evolver (Excel spreadsheet add-in) Genetic Algorithm User Interface (GAUI) OOGA (Object-Oriented GA for industrial use) XperRule Genasys (ES shell with an embedded genetic
algorithm) Sugal Genetic Algorithm Simulator
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Optimization Algorithms
Via neural computing sometimes
Genetic algorithms and their derivatives can optimize (or nearly optimize) complex problems
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Fuzzy Logic
Fuzzy logic deals with uncertainty
Uses the mathematical theory of fuzzy sets
Simulates the process of normal human reasoning
Allows the computer to behave less precisely and logically
Decision making involves gray areas and the term maybe
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Fuzzy Logic Advantages
Provides flexibility Provides options Frees the imagination More forgiving Allows for observation Shortens system development time Increases the system's maintainability Uses less expensive hardware Handles control or decision-making problems not
easily defined by mathematical models
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Fuzzy Logic Example:What is Tall?
In-Class ExerciseProportion
Height Voted for5’10” 0.055’11” 0.106’ 0.606’1” 0.156’2” 0.10
– Jack is 6 feet tall– Probability theory - cumulative probability – There is a 75 percent chance that Jack is tall
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Fuzzy logic - Jack's degree of membership within the set of tall people is 0.75
We are not completely sure whether he is tall or not Fuzzy logic - We agree that Jack is more or less tall Membership Function
< Jack, 0.75 Tall >
Knowledge-based system approach: Jack is tall (CF = .75)
Belief functions
Can use fuzzy logic in rule-based systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Membership Functions in Fuzzy Sets (Figure 16.11)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Membership
Short Medium Tall
Height in inches (1 inch = 2.54 cm)
0.5
1.0
64 69 74
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Fuzzy Logic Applications and
Software Difficult to apply when people provide evidence
Used in consumer products that have sensors– Air conditioners
– Cameras
– Dishwashers
– Microwaves
– Toasters
Special software packages
Controls applications
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Examples of Fuzzy Logic
Example 1: Strategic planning– STRATASSIST - fuzzy expert system that helps small- to
medium-sized firms plan strategically for a single product
Example 2: Fuzziness in real estate
Example 3: A fuzzy bond evaluation system
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Fuzzy Logic Software
Fuzzy Inference Development Environment (FIDE)
Z Search HyperLogic Corporation demos Others
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Qualitative Reasoning (QR)
– Means of representing and making inferences using general, physical knowledge about the world
– QR is a model-based procedure that consequently incorporates deep knowledge about a problem domain
– Typical QR Logic• “If you touch a kettle full of boiling water on a stove, you
will burn yourself”
• “If you throw an object off a building, it will go down”
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But
No specific knowledge about boiling temperature, just that it is really hot!
No specific information about the building or object, unless you are the object, or you are trying to catch it
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Main goal of QR: To represent common sense knowledge about the physical world, and the underlying abstractions used in quantitative models (objects fall)
Given such knowledge and appropriate reasoning methods, an ES could make predictions and diagnoses, and explain the behavior of physical systems qualitatively, even when exact quantitative descriptions are unavailable or intractable
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Relevant behavior is modeled
Temporal and spatial qualities in decision making are represented effectively
Applies common sense mathematical rules to variables and functions
There are structure rules and behavior rules
Qualitative Reasoning
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Some Real-World QR Applications
Nuclear plant fault diagnoses
Business processes
Financial markets
Economic systems
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Intelligent Systems Integration
Combine – Neural Computing
– Expert Systems
– Genetic Algorithms
– Fuzzy Logic
Example: International investment management--stock selection
Fuzzy Logic and ANN (FuzzyNet) to forecast the expected returns from stocks, cash, bonds, and other assets to determine the optimal allocation of assets
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Global markets Integrated network architecture of the system
(Figure 16.12)
Technologies Expert system (rule-based) for country and stock selection Neural network for forecasting Fuzzy logic for assessing factors without reliable data
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FuzzyNet Architecture
(Figure 16.12)
Membership Function Generator (MFG)
Fuzzy Information Processor (FIP)
Back-propagation Neural Network (BPN)
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Data Mining and KnowledgeDiscovery in Databases (KDD)
Hidden value in data Knowledge Discovery in Databases (KDD)
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The KDD ProcessStart with Raw Data and Do
1. Selection to produce target the appropriate data which undergoes
2. Preprocessing to filter the data in preparation for
3. Transformation so that
4. Data Mining can identify patterns that go through
5. Interpretation and Evaluation resulting in knowledge
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Data Mining
Find kernels of value in raw data ore
Theoretical advances
– Knowledge discovery in textual databases
– Methods based on statistics, cluster analysis, discriminant analysis, fuzzy logic, genetic algorithms, and neural networks
– Ideal for data mining
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AI Methods and Data Mining for Search
Neural Networks
Expert Systems
Rule Induction
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Data Mining Applications Areas
Marketing
Investment
Fraud detection
Manufacturing
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Information Overload
Data mining methods can sift through soft information to identify relationships automatically
Intelligent agents
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Important KDD andData Mining Challenges
Dealing with larger databases
Working with higher dimensionalities of data
Overfitting--modeling noise rather than data patterns
Assessing statistical significance of results
Working with constantly changing data and knowledge
Continue
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Working through missing and noisy data
Determining complex relationships between fields
Making patterns more understandable to humans
Providing better user interaction and prior knowledge about the data
Providing integration with other systems
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