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

2. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

3. 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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

4. 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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

6. Neural Network Credit Authorizer Construction 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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

7. 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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

8. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

9. Architecture of the Bankruptcy Prediction Neural Network (Figure 16.3)X1 X2
Bankrupt 0 X3
Not bankrupt 1 X4 X5
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

10. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

11. 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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

12. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

13. Integrated ANNs and Expert Systems
1. Resource Requirements Advisor
2. Personnel Resource Requirements Advisor
3. Diagnostic System for an Airline
4. Manufacturing Product Liability
5. Oil Refinery Production Scheduling and Environmental Control
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

14. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

15. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

16. Primary Operators of Most Genetic Algorithms
Reproduction
Crossover
Mutation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

17. Genetic Algorithm Operators
Parent 1
Parent 2
Child 1
Mutation
Child 2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

18. GA Example: The Knapsack Problem
Item:
Benefit:
Weight:
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:
Means choose items 1, 2, 5. Weight = 21, Benefit = 20
Evolver solution in Figure 16.10
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

19. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

21. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

22. Decision making involves gray areas and the term maybe
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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

23. 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)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

24. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

25. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

26. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Fuzzy Logic Software
Fuzzy Inference Development Environment (FIDE)
Z Search
HyperLogic Corporation demos
Others
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

27. 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”
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

28. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

29. Some Real-World QR Applications
Nuclear plant fault diagnoses
Business processes
Financial markets
Economic systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

30. 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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

31. Data Mining and Knowledge Discovery in Databases (KDD)
Hidden value in data
Knowledge Discovery in Databases (KDD)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ