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[资源] 【分享】The Fuzzy Systems Handbook.Academic Press.1994

The Fuzzy Systems Handbook_A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems
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Authors(Editors):
Earl Cox
Publisher: Academic Press
Pub Date:1994
Pages: 512
ISBN 0-12-194270-8

Preface
While several books are available today that address the mathematical and philosophical
foundations of fuzzy logic, none, unfortunately, provides the practicing knowledge
engineer, system analyst, and project manager with specific, practical information about
fuzzy system modeling. Those few books that include applications and case studies concentrate
almost exclusively on engineering problems: pendulum balancing, truck backeruppers,
cement kilns, antilock braking systems, image pattern recognition, and digital signal
processing. Yet the application of fuzzy logic to engineering problems represents only
a fraction of its real potential. As a method of encoding and using human knowledge in a
form that is very close to the way experts think about difficult, complex problems, fuzzy
systems provide the facilities necessary to break through the computational bottlenecks
associated with traditional decision support and expert systems. Additionally, fuzzy systems
provide a rich and robust method of building systems that include multiple conflicting,
cooperating, and collaborating experts (a capability that generally eludes not only
symbolic expert system users but analysts that have turned to such related technologies as
neural networks and genetic algorithms).
Yet the application of fuzzy logic in the areas of information technology, decision
support, and database analysis and mining has been largely ignored by both the commercial
vendors of decision support products and the knowledge engineers that use them.
Fuzzy logic has not found its way into the information modeling field due to a number of
factors that are rapidly changing—unfamiliarity with the concept, a predilection for the
use of confidence factors and Bayesian probabilities among most knowledge engineers
(stemming from the early successes of expert systems such as MYCIN, PROSPECTOR,
and XCON), and a suspicion that there is something fundamentally wrong with a reasoning
system that announces its own imprecision. Fuzzy logic is the essential oxymoron.
Fuzzy logic, however, is a technology that has patiently bided its time. Today, in the
world of highly complex, international business systems, webs of communications networks,
high-density information overloads, and the recognition that many seemingly simple
problems belie a deep nonlinearity, fuzzy logic is proving itself as a powerful tool in
knowledge modeling. Fuzzy logic will soon usher in the second wave of intelligent systems.
I have good reason to believe this prediction.
A little more than 13 years ago, while marketing an enterprise modeling system in the
United Kingdom, I was introduced to the idea of fuzzy logic by my friend Peter Llewelyn
Jones. Peter is the author of REVEAL, the first commercial fuzzy expert system and, with
Ian Graham, the author of Expert Systems: Knowledge, Uncertainty and Decision,1 one of
the very first books on fuzzy information systems. Sitting one evening in a pub just off
Fleet Street, and tucked neatly into the outskirts of Covent Gardens, about a block from
our office on the Waterloo Bridge, Peter explained in clear and convincing terms just why
fuzzy logic, in the more general form of approximate reasoning, was an important emerging
technology. Like Paul on the road to Damascus, a brilliant light went off in my mind
and I left the pub an eager devotee to the cult of fuzzy logic. Like all naive revolutionaries,
we expected the world to welcome our insights and revealed truths with open arms. However,
in spite of its evident potential and the success of many projects, REVEAL was
shelved by its owners and fuzzy logic remained the arcane study of Lotfi Zadeh and his
ever but slowly increasing circle of believers (usually graduate students who remained
well within the sheltering walls of Evans Hall high on a hill at the University of California
at Berkeley).
In the years since I was introduced to and began using fuzzy logic, I have seen firsthand
the power and breadth that fuzzy decision and expert systems bring to a wide spectrum
of unusually difficult problems. I have architected, designed, and programmed three
production fuzzy expert systems. These tools have been successfully applied to large, realworld
applications in such areas as transportation, managed health care, financial services,
insurance risk assessment, database information mining, company stability analysis, multiresource
and multiproject management, fraud detection, acquisition suitability studies,
new product marketing, and sales analysis. Generally, the final models were less complex,
smaller, and easier to build, implement, maintain, and extend than similar systems built
using conventional symbolic expert systems.

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