Computational Intelligence - February 2014 - 9

Under weak conditions, the proposed
neural network is proved to be stable,
and the state of the neural network is
convergent to one of its equilibrium
points. Compared with the existing
neural network models for nonsmooth optimization problems, the
proposed neural network can deal with
a larger class of constraints and is not
based on the penalty method. Finally,
the proposed neural network is used to
solve the identification problem of
genetic regulatory networks, which can
be transformed into a non-smooth
convex optimization problem. The simulation results show the satisfactory
identification accuracy, which demonstrates the effectiveness and efficiency
of the proposed approach.

IEEE Transactions on Fuzzy
Systems Outstanding Paper Award
Dongrui Wu and Jerry M. Mendel for
their paper entitled "On the Continuity of Type-1 and Interval Type-2 Fuzzy
Logic Systems", vol. 19, no. 1, pp. 179-
192, 2011.
Digital Object Identifier: 10.1109/
TFUZZ.2010.2091962
Abstract-This paper studies the
continuity of the input-output mappings
of fuzzy logic systems (FLSs), including
both type-1 (T1) and interval type-2
(IT2) FLSs. We show that a T1 FLS
being an universal approximator is
equivalent to saying that a T1 FLS has a
continuous input-output mapping. We
also derive the condition under which a
T1 FLS is discontinuous. For IT2 FLSs,

we consider six type-reduction and
defuzzification methods (the KarnikMendel method, the uncertainty bound
method, the Wu-Tan method, the NieTan method, the Du-Ying method, and
the Begian-Melek-Mendel method) and
derive the conditions under which continuous and discontinuous input-output
mappings can be obtained. Guidelines
for designing continuous IT2 FLSs are
also given. This paper is to date the most
comprehensive study on the continuity
of FLSs. Our results will be very useful
in the selection of the parameters of the
membership functions to achieve a
desired continuity (e.g., for most traditional modeling and control applications)
or discontinuity (e.g., for hybrid and
switched systems modeling and control).

Yaochu Jin
University of Surrey, UK

IEEE CIS VP-Technical Activities Vision Statement

t is my great pleasure to serve as Vice
President for Technical Activities of
the IEEE Computational Intelligence
Society (IEEE CIS). It is a great honor
to me, and a great responsibility as well.
I made my first experience with
fuzzy systems during my MSc study,
which were used to deal with uncertainties in control systems. Like many
other researchers in the early 1990s, I
was quickly attracted by the research
on neurofuzzy systems thanks to their
ability to learn the parameters in the
fuzzy rules and fuzzy membership
functions based on data. Due to limited
and delayed access to international
journals and conference proceedings at
that time in China, I was not aware of
the research on evolutionary algorithms
before I accidentally came across a
Digital Object Identifier 10.1109/MCI.2013.2291675
Date of publication: 14 January 2014

genetic algorithm described in a book
on robot trajectory planning in 1993. I
was immediately fascinated by the idea
of using a genetic algorithm to optimize both the structure and parameters
of neurofuzzy systems. My first paper
on neurofuzzy systems was published in
1992 that employed a neural network
as the membership functions of fuzzy
systems, and my first paper on genetic
fuzzy systems appeared in 1995, both in
Chinese. Even today, I am still very
proud of being able to publish two
papers in the IEEE Transactions in the
mid-1990s during my PhD study at
Zhejiang University, one on neurofuzzy systems and the other on genetic
fuzzy systems, both for dynamic control
of robot manipulators.
The terminology of Computational
Intelligence (CI) came to my attention
sometime in 1995 when I read the Call
for Papers of the First World Congress

on Computational Int el l igen ce
(WCCI) held in June 1994, Orlando,
Florida, USA, published in a journal in
1993. In 1998, I was so lucky to be able
to attend the second WCCI held in
May 1998 in Anchorage, Alaska, during
which I became a member of IEEE.
Since then, I have been increasingly
involved in activities of the IEEE. In
2004, I established a new Task Force
on "Evolutionary Computation in
Dynamic and Uncertain Environments"
within the Evolutionary Computation
Technical Committee (ECTC). I was
appointed by Prof. Gary Yen, the founding Editor-in-Chief of the IEEE Computational Intelligence Magazine to be
an Associate Editor of the new journal
in 2006. I was invited to be a member
of both the ECTC and the Emerging
Technology Technical Committee
(ETTC) in 2007, and became
Chair of the Continuing Education

February 2014 | Ieee ComputatIonal IntellIgenCe magazIne

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