Book Announcement

[Yaochu Jin]

A new book titled "Advanced Fuzzy Systems Design and Applications",
published by Springer/Physica Verlag (ISBN: 3-7908-1537-3) is coming
out in December 16, 2002.

Abstract

Fuzzy rule systems have found a wide range of applications in
many fields of science and technology. Traditionally, fuzzy
rules are generated from human expert knowledge or human heuristics
for relatively simple systems. In the last few years, data-driven fuzzy
rule generation has been very active. Compared to heuristic fuzzy rules,
fuzzy rules generated from data are able to extract more profound
knowledge for more complex systems.

This book presents a number of approaches to the generation of fuzzy
rules from data, ranging from the direct fuzzy inference based to
neural networks and evolutionary algorithms based fuzzy rule
generation. Besides the approximation accuracy, special attention has
been paid to the interpretability of the extracted fuzzy rules.  In
other words, the fuzzy rules generated from data are supposed to be as
comprehensible to human beings as those generated from human
heuristics.  To this end, many aspects of interpretability of fuzzy
systems have been discussed, which must be taken into account in the
data-driven fuzzy rule generation. In this way, fuzzy rules generated
from data are intelligible to human users and therefore, knowledge
about unknown systems can be extracted.

The other direction of knowledge extraction from data in terms of
interpretable fuzzy rules is the incorporation of human knowledge into
learning and evolutionary systems with the help of fuzzy logic.
In this book, methods for embedding human knowledge, which can be
represented either by fuzzy rules or fuzzy preference models, into
neural network learning and evolutionary multiobjective optimization
have been introduced. Thus, neural networks and evolutionary
algorithms are able to take advantage of data as well as human
knowledge.

In this book, fuzzy rules are designed mainly for modeling, control
and optimization. Along with the discussion of the methods, several
real-world application examples in the above fields, including
robotics, process control and intelligent vehicle systems are
described. Illustrative figures are also given to accompany the most
important methods and concepts.  To make the book self-contained,
fundamental theories as well as a few selected advanced topics about
fuzzy systems, neural networks and evolutionary algorithms have been
provided. Therefore, this book is a valuable reference for
researchers, practitioners and students in many fields of science and
engineering.

Publisher Website:
http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1537-3
Amazon:
http://www.amazon.com/exec/obidos/tg/detail/-/3790815373/qid=1034592300/sr=8-2/ref=sr_8_2/002-8946315-4188058?v=glance&n=507846

Main Contents

Preface
Chapter 1 Fuzzy Sets and Fuzzy Systems
Chapter 2 Evolutionary Algorithms
Chapter 3 Artificial Neural Networks
Chapter 4 Conventional Data-driven Fuzzy systems Design
Chapter 5 Neural Network Based Fuzzy Systems Design
Chapter 6 Evolutionary Design of Fuzzy Systems
Chapter 7 Knowledge Discovery by Extracting Interpretable Fuzzy Rules
Chapter 8 Fuzzy Knowledge Incorporation into Neural Networks
Chapter 9 Fuzzy Preferences Incorporation into Multiobjective
Optimization

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Dr. Yaochu Jin
Future Technology Research
Honda R&D Europe (D)
Carl-Legien-Str. 30
63073 Offenbach/Main
GERMANY
Tel: +49 69 89011735
Fax: +49 69 89011749
Email: yaochu.jin at hre-ftr.f.rd.honda.co.jp