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Fuzzy Logic and NeuroFuzzy Applications in Business and Finance
- By Constantin von Altrock
- Published Nov 18, 1996 by Pearson.
Book
- Sorry, this book is no longer in print.
Description
- Copyright 1997
- Dimensions: 7 X 9-1/4
- Pages: 400
- Edition: 1st
- Book
- ISBN-10: 0-13-591512-0
- ISBN-13: 978-0-13-591512-7
59151-1
Make sharper business decisions with fuzzy logic!
What's it worth to your business to make better decisions about:
- Credit?
- R&D?
- Investments?
- Direct marketing?
- Suppliers?
- Quality control?
You can do all that, and a whole lot more, with fuzzy logic, the first technology that allows computers to make decisions with human-like judgment. Long proven in engineering and scientific applications, fuzzy logic takes into account all the uncertainties of the real world—where “maybe” and “probably” are a whole lot more common than “yes” and “no”
Now, internationally-renowned fuzzy logic expert Constantin Von Altrock, author of Fuzzy Logic and NeuroFuzzy Applications Explained, shows you exactly how fuzzy logic works—and gives you tools to use it in your business.
In this hands-on, practical guide, you'll walk through powerful fuzzy logic business applications for business, including supplier evaluation, customer targeting, scheduling, choosing R&D projects, and forecasting. You'll watch fuzzy logic at work analyzing credit, evaluating leases, making stock market decisions, and uncovering fraud.
Then model your own fuzzy logic applications with fuzzyTECH™ for Business simulation software for Windows™. It's included on the CD-ROM—along with working applications you can adapt for your own needs. You'll even learn how to integrate fuzzy logic decision-making with your existing data, using Microsoft's Visual Basic, Access, Excel, OLE, and DDE.
If you need to make the best possible decisions, make them fast, and make plenty of them, you've come to the right book: Fuzzy Logic and NeuroFuzzy Applications in Business and Finance.
Sample Content
Table of Contents
1. Fuzzy Logic Primer.
The Fuzzy Logic Benefit.
Sample Applications—Types of Uncertainty—A “Fuzzy” Set.
A Case Study on Fuzzy Logic Inference.
Financial Liquidity Evaluation Example—Conventional Decision Support Techniques—Linguistic Decision making.
The Fuzzy Logic Algorithm.
Fuzzification Using Linguistic Variables—Fuzzy Logic Inference Using If-Then Rules—Defuzzification Using Linguistic Variables.
More Fuzzy Logic Theory.
2. Getting Started with fuzzyTECH for Business.
Installation Guide.
License Agreement—Installing fuzzyTECH and the Samples—Conventions—First Steps.
Basic System Design Methodology.
Using the Fuzzy Design Wizard—Creating a Rule Base— Interactive Debugging—File Debugging and Analyzers.
Extending the System.
Adding New Components—Interactive Debugging of Complex Projects—Advanced Features of fuzzyTECH—fuzzyTECH's Revision Control System—Creating Stand-Alone Solutions.
3. Getting Started with NeuroFuzzy Design.
NeuroFuzzy Technology.
Adaptive Systems and Neural Networks—Combining Neural and Fuzzy—NeuroFuzzy vs. Other Adaptive Technologies.
Training Examples.
Using the fuzzyTECH NeuroFuzzy Module—Training the Creditworthiness Evaluation—NeuroFuzzy Training in Data Analysis.
Data Clustering.
Clustering Techniques—Clustering with fuzzyTECH—Fuzzy Clustering of NeuroFuzzy Training Data.
4. Integration of Fuzzy Logic with Standard Software.
Using DDE and DLL links with fuzzyTECH.
Integration Link Overview—DDE Link—Programming fuzzyTECH Using the DLL Link.
Integration of fuzzyTECH with MS-Excel.
Installing the fuzzyTECH Assistant—Creating a Fuzzy Logic Spreadsheet—Stocks Analysis Case Study.
Integration of fuzzyTECH with VisualBasic.
Single Call Remote Interface Using VisualBasic—Standard Call Remote Interface Using VisualBasic—A Case Study Using VisualBasic.
Integration of fuzzyTECH with MS-Access.
Integration of Fuzzy Logic Functions—The FT Investment Bank Case Study—FT Investment Bank's MS-Access Database— AccessBasic Integration.
5. Case Studies of Fuzzy Logic Applications.
Fuzzy Logic in Finance Applications.
Fuzzy Scoring for Mortgage Applicants—Creditworthiness Assessment—Fraud Detection—Other Finance Applications.
Fuzzy Logic in Business Applications.
Supplier Evaluation for Sample Testing—Customer Targeting—Sequencing and Scheduling—Optimizing Research and Development Projects—Knowledge-Based Prognosis.
Fuzzy Logic in Data Analysis Applications.
Fuzzy Data Analysis in Cosmetics—Other Fuzzy Data Analysis Applications.
6. Advanced Fuzzy Logic Design Techniques.
Linguistic Variables and Their Membership Functions.
Design Methodology of Linguistic Variables—Linear Standard Membership Functions—Membership Function Shapes.
Fuzzy Interfaces.
Defining Fuzzy Interfaces—Building Explanatory Components.
Fuzzy Inference Methods.
Premise Aggregation with Fuzzy Logic Operators—Result Aggregation—Matrix Rule Representation.
Defuzzification Methods.
Best Compromise vs. Most Plausible Result—Comparison of Defuzzification Methods—Information Reduction by Defuzzification.
7. Bibliography.
8. Index.
Preface
A crucial factor in competing in business in the 21st century will be clever use of information technology. Today's information technology systems are mostly data and communication tools for human workers. Tomorrow's information technology systems will be able to do more: automate decisions, intelligently analyze large amounts of data, and learn from their mistakes. Such systems need to have a better way of representing the logic and rationale behind human thinking. Central to this revolution will be finding a way to program a computer to express human-like decisions and evaluations in human language. For this you need fuzzy logic.
In this book, I take the practitioner's approach to fuzzy logic and NeuroFuzzy techniques. I will explain all elements of fuzzy logic system design using case studies of real-world applications-no formulas, no complex math, just everything you need for a hands-on start. Roll up your sleeves, and I will guide you step-by-step through fuzzy logic and NeuroFuzzy design on your PC. On the attached CD-ROM you will find a simulation-only version of a professional fuzzy logic design software and the source code of many real-world case studies I discuss. In just hours, these tools will get you design solutions without any programming.
I would like to thank the “fathers” of fuzzy set theory, Lotfi Zadeh, Hans Zimmermann, and Enrique Ruspini, who introduced me to fuzzy logic in 1983 and for their continuous support and encouragement of my work. I would also like to thank everyone at the Fuzzy Technology division of INFORM Software Corp. for their innovative and productive work on the fuzzyTECH software tools and customer application projects. In particular I would like to thank Adrian Weiler, who gave me the chance to build up the Fuzzy Technology division in 1990 when the broad success of fuzzy logic was not as clear as it is today, and Bernhard Krause, who built up the business with me.
Constantin von Altrock M.Sc.E.E., M.O.R.