Title: Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications
1CHAPTER 16
- Neural Computing Applications, and Advanced
Artificial Intelligent Systems and Applications
2Neural 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)
3Areas of ANN ApplicationsAn Overview
- Representative Business ANN Applications
- Accounting
- Finance
- Human Resources
- Management
- Marketing
- Operations
4Accounting
- Identify tax fraud
- Enhance auditing by finding irregularities
5Finance
- 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
6Finance 2
- Credit card profitability
- Forecasting economic turning points
- Bond rating and trading
- Pricing initial public offerings
- Loan approvals
- Economic and financial forecasting
- Risk management
7Human Resources
- Predicting employees performance and behavior
- Determining personnel resource requirements
8Management
- Corporate merger prediction
- Country risk rating
9Marketing
- Consumer spending pattern classification
- Customers characteristics
- Sales forecasts
- Data mining
- Airline fare management
- Direct mail optimization
- Targeted marketing
10Operations
- 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
11Credit Approval with Neural Networks
- Increases loan processor productivity by 25 to
35 over other computerized tools - Also detects credit card fraud
12The 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
13Neural 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
14Bankruptcy 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
15Application 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
17Architecture 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
20Stock 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
22Network 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
24Integrated 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
29Genetic 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
30Genetic 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
31Primary Operators of Most Genetic Algorithms
- Reproduction
- Crossover
- Mutation
32Genetic 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
33GA 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
34Genetic AlgorithmsApplications and Software
- Type of machine learning
- Set of efficient, domain-independent search
heuristics for a broad spectrum of applications
35Genetic 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
36Business 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
37Representative 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
38Optimization Algorithms
- Via neural computing sometimes
- Genetic algorithms and their derivatives can
optimize (or nearly optimize) complex problems
39Fuzzy 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
40Fuzzy 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
41Fuzzy 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
43Membership 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)
44Fuzzy 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
45Examples 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
46Fuzzy Logic Software
- Fuzzy Inference Development Environment (FIDE)
- Z Search
- HyperLogic Corporation demos
- Others
47Qualitative 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
50Qualitative 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
51Some Real-World QR Applications
- Nuclear plant fault diagnoses
- Business processes
- Financial markets
- Economic systems
52Intelligent 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
54FuzzyNet Architecture (Figure 16.12)
- Membership Function Generator (MFG)
- Fuzzy Information Processor (FIP)
- Back-propagation Neural Network (BPN)
55Data Mining and KnowledgeDiscovery in Databases
(KDD)
- Hidden value in data
- Knowledge Discovery in Databases (KDD)
56The 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
57Data 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
58AI Methods and Data Mining for Search
- Neural Networks
- Expert Systems
- Rule Induction
59Data Mining Applications Areas
- Marketing
- Investment
- Fraud detection
- Manufacturing
60Information Overload
- Data mining methods can sift through soft
information to identify relationships
automatically - Intelligent agents
61Important 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