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)