RE: BISC_SIG_ES, Smart Controller, Open Discussion

masoud nikravesh (
Tue, 17 Jun 1997 14:51:54 +0200

Dear Biscers;

Now the follwoing subjects are open to discussion.

*****Mining and Fusion of Complex Data with Fuzzy Logic and Neural Network

*****Knowledge Discovery from Data Bases: Intelligent Data Mining Technique

BISC_SIG_ES is accepting Abstract (Max. 1 Page), Extended Abstract (Max. 2
Pages) and Short Paper (Max. 5 Pages).
Also, If you would like to be considerd as part of our multi-objectives
proposal and project, please send an extra page (Max. 2 pages) describing
type of contribution to BISC_SIG_ES Knowledge Discovery Project.

Your comments and suggestions are appreciated. Please feel free to contact

Please use " BISC_SIG_ES Knowledge Discovery" as the subject of the mail, and
include the
following information in the body if possible:

Field of Interest:
Mailing address:
Preferred E-mail address:
Http address:

There is a homepage which you could find out more information about the BISC. , , ,

Masoud Nikravesh, Ph.D.
Chairman, BISC Special Interest Group In Earth Sciences
BISC is an acronym for the Berkeley Initiative in Soft Computing.

Earth Sciences Division, MS 90-1116, Lawrence Berkeley National Laboratory
and Electrical Engineering and Computer Science Department, BISC Program, Soda
University of California at Berkeley
Berkeley, CA 94720


Tel: (510) 486-7728
Fax: (510) 486-5686
Fax: (510) 642-3805


Retrieving the information content of enormous and complex data bases in an
efficient manner is a formidable task. Equally important is the ability to
react appropriately, intelligently and in real time while data mining and
information retrieval is in progress as an "on-line expert consultant". We are
proposing to tackle this problem by developing and integrating
state-of-the-art methodology for Data Fusion and Mining of massive data-sets
and Extract Knowledge from data-intensive knowledge domains. This intelligent
expert system will generate the appropriate response or reaction as it is
conducting the data mining task. Our Fuzzy, Intelligent, Neural Expert System
with Search Engine (FINESSE) will be a hybrid system based on: (1) fuzzy logic
for its tolerance for imprecision which can bring to bear on the process of
knowledge acquisition from massive data-sets, (2) neural networks to extract
and fine-tune the fuzzy rules and the characteristics of the membership
functions, and because it is a fundamentally parallel technique, and (3)
genetic algorithms for extraction of patterns, structure, and reduce the
complexity of the neuro-fuzzy model. Our expectation is that it will
significantly improve data management and information recovery from huge
corporate databases

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