Neuro-Fuzzy and Soft Computing
Foreword by Prof.
Among my many Ph.D. students, some have forged new tools in their work.
J.-S. Roger Jang and C.-T. Sun fall into this category.
"Neuro-Fuzzy and Soft Computing" makes visible their mastery
of the subject matter, their insightfulness and their expository skill.
Their co-author, Eiji Mizutani, has made an important contribution by
bringing to the writing of the text his extensive experience in
dealing with real-world problems in an industrial setting.
"Neuro-Fuzzy and Soft Computing," is one of the first texts to focus
on soft computing -- a concept which has direct bearing on machine
intelligence. In this connection, a bit of history is in order.
The concept of soft computing began to crystallize during the past
several years and is rooted in some of my earlier work on soft data
analysis, fuzzy logic and intelligent systems. Today, close to four
decades after AI was born, it can finally be said with some
justification that intelligent systems are becoming a reality.
Why did it take so long for the era of intelligent systems to arrive?
In the first place, the AI community had greatly underestimated the
difficulty of attaining the ambitious goals which were on its agenda.
The needed technologies were not in place and the conceptual tools in
AI's armamentarium -- mainly predicate logic and symbol manipulation
techniques -- were not the right tools for building machines which could
be called intelligent in a sense that matters in real-world
Today we have the requisite hardware, software and sensor technologies
at our disposal for building intelligent systems. But, perhaps more
importantly, we are also in possession of computational tools which
are far more effective in the conception and design of intelligent
systems than the predicate-logic-based methods which form the core of
traditional AI. The tools in question derive from a collection of
methodologies which fall under the rubric of what has come to be known
as soft computing (SC). In large measure, it is the employment of soft
computing techniques that underlies the rapid growth in the variety
and visibility of consumer products and industrial systems which
qualify to be assessed as possessing significantly high
MIQ (Machine Intelligence Quotient).
The essence of soft computing is that unlike the traditional, hard
computing, soft computing is aimed at an accommodation with the
pervasive imprecision of the real world. Thus, the guiding principle
of soft computing is: Exploit the tolerance for imprecision,
uncertainty and partial truth to achieve tractability, robustness,
low solution cost and better rapport with reality. In the final
analysis, the role model for soft computing is the human mind.
Soft computing (SC) is not a single methodology. Rather, it is a
partnership. The principal partners at this juncture are fuzzy logic
(FL), neurocomputing (NC) and probabilistic reasoning (PR), with the
latter subsuming genetic algorithms (GA), chaotic systems, belief
networks and parts of learning theory. The pivotal contribution of FL
is a methodology for computing with words; that of NC is system
identification, learning and adaptation; that of PR is propagation of
belief; and that of GA is systematized random search and optimization.
In the main, FL, NC and PR are complementary rather than competitive.
For this reason, it is frequently advantageous to use FL, NC and PR in
combination rather than exclusively, leading to so-called hybrid
intelligent systems. At this juncture, the most visible systems of
this type are neuro-fuzzy systems. We are also beginning to see
fuzzy-genetic, neuro-genetic and neuro-fuzzy-genetic systems.
Such systems are likely to become ubiquitous in the not distant future.
In coming years, the ubiquity of intelligent systems is certain to
have a profound impact on the ways in which man-made systems are
conceived, designed, manufactured, employed and interacted with.
This is the perspective in which the contents of "Neuro-Fuzzy and Soft
Computing" should be viewed.
Taking a closer look at the contents of
"Neuro-Fuzzy and Soft Computing," what should be noted is that, today,
most of the applications of fuzzy logic involve what might be called
the calculus of fuzzy rules or CFR, for short. To a considerable
degree, CFR is self-contained. Furthermore, CFR is relatively easy to
master because it is close to human intuition. Taking advantage of
this, the authors focus their attention on CFR and minimize the time
and effort that are needed to acquire sufficient expertise in
fuzzy logic to be able to apply it to real-world problems.
One of the central issues in CFR is the induction of rules from
observations. In this context, neural network techniques and
genetic algorithms play pivotal roles which are discussed in
"Neuro-Fuzzy and Soft Computing," in considerable detail and a great
deal of insight. In the application of neural network techniques,
the main tool is that of gradient programming. By contrast, in the
application of genetic algorithms, simulated annealing and random
search methods, the existence of a gradient is not assumed.
The complementarity of gradient programming and gradient-free methods
provides a basis for the conception and design of neuro-
A notable contribution of "Neuro-Fuzzy and Soft Computing," is the
exposition of ANFIS (Adaptive-Network-based Fuzzy Inference System) --
a system developed by the authors which is finding numerous
applications in a variety of fields. ANFIS and its variants and
relatives in the realms of neural, neuro-fuzzy and reinforcement
learning systems represent a direction of basic importance in the
conception and design of intelligent systems with high MIQ.
"Neuro-Fuzzy and Soft Computing" is a thoroughly up-to-date text with
a wealth of information which is well-organized, clearly presented
and illustrated by many examples. It is a must reading for anyone
who is interested in acquiring a solid background in soft computing --
a partnership of methodologies which play pivotal roles in the
conception, design and application of intelligent systems.
Lotfi A. Zadeh
July 28, 1995
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