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Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications

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and Advanced Artificial Intelligent Systems and Applications ... Fujitsu and Nikko Securities: TOPIX Buying and Selling Prediction System. 21 ... – PowerPoint PPT presentation

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Title: Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications


1
CHAPTER 16
  • Neural Computing Applications, and Advanced
    Artificial Intelligent Systems and Applications

2
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)


3
Areas of ANN ApplicationsAn Overview
  • Representative Business ANN Applications
  • Accounting
  • Finance
  • Human Resources
  • Management
  • Marketing
  • Operations


4
Accounting
  • Identify tax fraud
  • Enhance auditing by finding irregularities


5
Finance
  • 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


6
Finance 2
  • Credit card profitability
  • Forecasting economic turning points
  • Bond rating and trading
  • Pricing initial public offerings
  • Loan approvals
  • Economic and financial forecasting
  • Risk management


7
Human Resources
  • Predicting employees performance and behavior
  • Determining personnel resource requirements


8
Management
  • Corporate merger prediction
  • Country risk rating


9
Marketing
  • Consumer spending pattern classification
  • Customers characteristics
  • Sales forecasts
  • Data mining
  • Airline fare management
  • Direct mail optimization
  • Targeted marketing


10
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


11
Credit Approval with Neural Networks
  • Increases loan processor productivity by 25 to
    35 over other computerized tools
  • Also detects credit card fraud


12
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


13
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


14
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


15
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


16
  • 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


17
Architecture of the Bankruptcy Prediction Neural
Network(Figure 16.3)
X1
X2
Bankrupt 0
X3
Not bankrupt 1
X4
X5

18
  • 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


19
  • 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


20
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


21
  • Input Several technical and economic indexes
  • Several modular neural networks relate past
    indexes, and buy/sell timing
  • Prediction system
  • Modular neural networks
  • Very accurate


22
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


23
  • 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


24
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


25
  • 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)


26
  • 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)


27
  • 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)


28
  • 5. Oil Refinery Production Scheduling and
    Environmental Control
  • Citgo Petroleum Corporation
  • Lower costs
  • Improved safety
  • Higher product quality
  • Higher yields


29
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


30
Genetic AlgorithmDefinition 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


31
Primary Operators of Most Genetic Algorithms
  • Reproduction
  • Crossover
  • Mutation


32
Genetic Algorithm Operators
1 0 1 0 1 1 1
Parent 1
1 1 0 0 0 1 1
Parent 2
1 0 1 0 0 1 1
Child 1
Mutation
1 1 0 0 1 1 0
Child 2

33
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 1s
  • Solution 1 1 0 0 1 0 0
  • Means choose items 1, 2, 5. Weight 21, Benefit
    20
  • Evolver solution in Figure 16.10


34
Genetic AlgorithmsApplications and Software
  • Type of machine learning
  • Set of efficient, domain-independent search
    heuristics for a broad spectrum of applications


35
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


36
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


37
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


38
Optimization Algorithms
  • Via neural computing sometimes
  • Genetic algorithms and their derivatives can
    optimize (or nearly optimize) complex problems


39
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


40
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


41
Fuzzy Logic ExampleWhat is Tall?
  • In-Class Exercise
  • Proportion
  • Height Voted for
  • 510 0.05
  • 511 0.10
  • 6 0.60
  • 61 0.15
  • 62 0.10
  • Jack is 6 feet tall
  • Probability theory - cumulative probability
  • There is a 75 percent chance that Jack is tall


42
  • 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 lt Jack, 0.75 ? Tall gt
  • Knowledge-based system approach Jack is tall
    (CF .75)
  • Belief functions
  • Can use fuzzy logic in rule-based systems


43
Membership Functions in Fuzzy Sets (Figure 16.11)
Short
Medium
Tall
1.0
Membership
0.5
64
74
69
Height in inches (1 inch 2.54 cm)

44
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


45
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


46
Fuzzy Logic Software
  • Fuzzy Inference Development Environment (FIDE)
  • Z Search
  • HyperLogic Corporation demos
  • Others


47
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


48
  • 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


49
  • 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


50
Qualitative Reasoning
  • 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


51
Some Real-World QR Applications
  • Nuclear plant fault diagnoses
  • Business processes
  • Financial markets
  • Economic systems


52
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


53
  • 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


54
FuzzyNet Architecture (Figure 16.12)
  • Membership Function Generator (MFG)
  • Fuzzy Information Processor (FIP)
  • Back-propagation Neural Network (BPN)


55
Data Mining and KnowledgeDiscovery in Databases
(KDD)
  • Hidden value in data
  • Knowledge Discovery in Databases (KDD)


56
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


57
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


58
AI Methods and Data Mining for Search
  • Neural Networks
  • Expert Systems
  • Rule Induction


59
Data Mining Applications Areas
  • Marketing
  • Investment
  • Fraud detection
  • Manufacturing


60
Information Overload
  • Data mining methods can sift through soft
    information to identify relationships
    automatically
  • Intelligent agents


61
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

62
  • 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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