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CHAPTER 16 Neural Computing Applications, and Advanced Artificial Intelligent Systems and...
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Transcript of CHAPTER 16 Neural Computing Applications, and Advanced Artificial Intelligent Systems and...
CHAPTER 16
Neural Computing Applications, and Advanced Artificial Intelligent
Systems and Applications
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)
Areas of ANN Applications:An Overview
Representative Business ANN Applications
Accounting Finance Human Resources Management Marketing Operations
Credit Approval with Neural Networks
Increases loan processor productivity by 25 to 35 % over other computerized tools
Also detects credit card fraud
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
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
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
Application Design
Five Input NodesX1: Working capital/total assets X2: Retained earnings/total assetsX3: Earnings before interest and taxes/total assetsX4: Market value of equity/total debtX5: Sales/total assets
Single Output Node: Final classification for each firm – Bankruptcy or – Nonbankruptcy
Development Tool: NeuroShell
Architecture of the Bankruptcy Prediction Neural Network
(Figure 16.3)
X4
X3
X5
X1
X2Bankrupt 0
Not bankrupt 1
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
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
Input: Several technical and economic indexes
Several modular neural networks relate past indexes, and buy/sell timing
Prediction system– Modular neural networks
– Very accurate
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
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
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
Primary Operators of Most Genetic Algorithms
Reproduction
Crossover
Mutation
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
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
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
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
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
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
Membership Functions in Fuzzy Sets (Figure 16.11)
Membership
Short Medium Tall
Height in inches (1 inch = 2.54 cm)
0.5
1.0
64 69 74
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
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
Fuzzy Logic Software
Fuzzy Inference Development Environment (FIDE)
Z Search HyperLogic Corporation demos Others
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”
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
Some Real-World QR Applications
Nuclear plant fault diagnoses
Business processes
Financial markets
Economic systems
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
Data Mining and KnowledgeDiscovery in Databases (KDD)
Hidden value in data Knowledge Discovery in Databases (KDD)