The ability of fuzzy systems to provide shades of gray between "on
or off" and "yes or no" is ideally suited to many of todaya (TM)s
complex industrial control systems. The static fuzzy systems
usually discussed in this context fail to take account of inputs
outside a pre-set range and their off-line nature makes tuning
complicated.
Advanced Fuzzy Logic Technologies in Industrial Applications
addresses the problem by introducing a dynamic, on-line fuzzy
inference system. In this system membership functions and control
rules are not determined until the system is applied and each
output of its lookup table is calculated based on current
inputs.
The tuning process is a major focus in this volume because it is
the most difficult stage in fuzzy control application. Using new
methods such as A-law technique, histogram equalization and the
Bezier-based method, all detailed here, the tuning process can be
significantly simplified and control performance improved.
The other great strength of this book lies in the range and
contemporaneity of its applications and examples which include:
laser tracking and control; robot calibration; image processing and
pattern recognition; medical engineering; audio systems; autonomous
underwater vehicles and data mining.
Advanced Fuzzy Logic Technologies in Industrial Applications is
written to be easily understood by readers not having specialized
knowledge of fuzzy logic and intelligent control. Design and
application engineers and project managers working in control, as
well as researchers and graduate students in the discipline will
find much to interest them in this work.
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