Download GRID Feedback 2002 No.2 - Delft Center for Systems and ...

A possible future direction is to focus on the development of design-support tools that
will automatically generate alternative solutions (models, controllers) to the problem
at hand. By means of an autonomous commissioning system, several of these
solutions could then be deployed simultaneously in some kind of self-organizing
controller (by using evolutionary programming, for instance). A step further would be
the integration of this concept with the design of the process itself.
Sensors and cognition
Biological organisms are equipped with a highly efficient, redundant system for
sensing the environment, processing and storing the acquired information. This is
something we still can only dream of in the case of man-made systems. Clearly, a
major progress can only be achieved with the use of new, much more affordable
sensor technologies. However, along with better sensors, we need to develop tools
for interpreting the acquired data and storing them in the form of knowledge.
Step by step?
AI techniques, such as neural networks, fuzzy logic, genetic algorithms, etc., have
undoubtedly contributed to the progress of automatic control by gradually extending
the functionality of more traditional control systems. However, the intriguing question
is whether these small incremental improvements in control will eventually lead to the
synthesis of truly intelligent systems. Some believe they will, others argue that this is
like trying to build an airplane by successively improving the car engine. It is difficult
to predict the future developments, but, at this moment, it seems quite certain that for
the next several decades, intelligent control will remain one of the most challenging
fields of engineering and science.
Contact: r.babuska@its.tudeft.nl, http://Lcewww.et.tudelft.nl/~babuska
Further reading
Harris, C.J., C.G. Moore and M. Brown (1993). Intelligent Control, Aspects of Fuzzy
Logic and Neural Nets. World Scientific, Singapore.
Haykin, S. (1994). Neural Networks. Macmillan Maxwell International, New York.
Jang, J.-S.R., C.-T. Sun and E. Mizutani (1997). Neuro-Fuzzy and Soft Computing; a
Computational Approach to Learning and Machine Intelligence. Prentice-Hall,
Upper Saddle River.
Klir, G.J. and B. Yuan (1995). Fuzzy sets and fuzzy logic; theory and applications.
Prentice Hall, Upper Saddle River.
Passino, K. M. and S. Yurkovich (1998). Fuzzy Control. Addison-Wesley,
Massachusetts.
Zurada, Jacek M., Robert J. Marks II and Charles J. Robinson (Eds.) (1994).
Computational Intelligence: Imitating Life. IEEE Press, Piscataway, NJ.
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