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 fuzzyTECHaA A for Business simulation software for WindowsaA A . 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.

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.