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

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Presentation on theme: "CHAPTER 16 Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications."— Presentation transcript:

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

2 n Several Real-World Applications of ANN Technology n Advanced AI Systems –Genetic Algorithms –Fuzzy Logic –Qualitative Reasoning n Integration (Hybrids)

3 Areas of ANN Applications: An Overview Representative Business ANN Applications n Accounting n Finance n Human Resources n Management n Marketing n Operations

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

5 The ANN Method n Data from the application and into a database n Preprocess applications manually n Neural network trained in advance with many good and bad risk cases

6 Neural Network Credit Authorizer Construction Process n Step 1: Collect data n Step 2: Separate data into training and test sets n Step 3: Transform data into network inputs n Step 4: Select, train, and test network n Step 5: Deploy developed network application

7 Bankruptcy Prediction with Neural Networks Concept Phase n Paradigm: Three-layer network, back-propagation n Training data: Small set of well-known financial ratios n Data available on bankruptcy outcomes n Supervised network n Training time not to be a problem

8 Application Design n 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 n Single Output Node: Final classification for each firm –Bankruptcy or –Nonbankruptcy n Development Tool: NeuroShell

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

10 n ANN did better predicting 22 out of the 27 actual cases n Discriminant analysis predicted only 16 correctly n Error Analysis –Five bankrupt firms misclassified by both methods –Similar for nonbankrupt firms n Neural network at least as good as conventional n Accuracy of about 80 percent is usually acceptable for neural network applications

11 Stock Market Prediction System with Modular Neural Networks n Accurate Stock Market Prediction - Complex Problem n Several Mathematical Models - Disappointing Results n Fujitsu and Nikko Securities: TOPIX Buying and Selling Prediction System

12 n Input: Several technical and economic indexes n Several modular neural networks relate past indexes, and buy/sell timing n Prediction system –Modular neural networks –Very accurate

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

14 Genetic Algorithms n Goal (evolutionary algorithms): Demonstrate self- organization and adaptation by exposure to the environment n System learns to adapt to changes. n Example 1: Vector Game –Random trial and error –Genetic algorithm solution n Process (Figure 16.9) n Example: the game of MasterMind

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) n Each candidate solution is called a chromosome n Chromosomes can copy themselves, mate, and mutate n Use specific genetic operators - reproduction, crossover and mutation

16 Primary Operators of Most Genetic Algorithms n Reproduction n Crossover n Mutation

17 Genetic Algorithm Operators Parent 1 Parent Child 1 Child 2 Mutation

18 GA Example: The Knapsack Problem n Item: n Benefit: n Weight: n Knapsack holds a maximum of 22 pounds n Fill it to get the maximum benefit n Solutions take the form of a string of 1s n Solution: n Means choose items 1, 2, 5. Weight = 21, Benefit = 20 n Evolver solution in Figure 16.10

19 Genetic Algorithm Application Areas n Dynamic process control n Induction of rule optimization n Discovering new connectivity topologies n Simulating biological models of behavior and evolution n Complex design of engineering structures n Pattern recognition n Scheduling n Transportation n Layout and circuit design n Telecommunication n Graph-based problems

20 Business Applications n Channel 4 Television (England) to schedule commercials n Driver scheduling in a public transportation system n Jobshop scheduling n Assignment of destinations to sources n Trading stocks n Productivity in whisky making is increased n Often genetic algorithm hybrids with other AI methods

21 Representative Commercial Packages n Evolver (Excel spreadsheet add-in) n Genetic Algorithm User Interface (GAUI) n OOGA (Object-Oriented GA for industrial use) n XperRule Genasys (ES shell with an embedded genetic algorithm) n Sugal Genetic Algorithm Simulator

22 Fuzzy Logic n Fuzzy logic deals with uncertainty n Uses the mathematical theory of fuzzy sets n Simulates the process of normal human reasoning n Allows the computer to behave less precisely and logically Decision making involves gray areas and the term maybe

23 Membership Functions in Fuzzy Sets (Figure 16.11) Membership ShortMediumTall Height in inches (1 inch = 2.54 cm)

24 Fuzzy Logic Applications and Software n Difficult to apply when people provide evidence n Used in consumer products that have sensors –Air conditioners –Cameras –Dishwashers –Microwaves –Toasters n Special software packages n Controls applications

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

26 Fuzzy Logic Software n Fuzzy Inference Development Environment (FIDE) n Z Search n HyperLogic Corporation demos n Others

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

28 n But n No specific knowledge about boiling temperature, just that it is really hot! n No specific information about the building or object, unless you are the object, or you are trying to catch it

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

30 Intelligent Systems Integration n Combine –Neural Computing –Expert Systems –Genetic Algorithms –Fuzzy Logic n Example: International investment management-- stock selection n Fuzzy Logic and ANN (FuzzyNet) to forecast the expected returns from stocks, cash, bonds, and other assets to determine the optimal allocation of assets

31 Data Mining and Knowledge Discovery in Databases (KDD) n Hidden value in data n Knowledge Discovery in Databases (KDD)

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