IEEE Computational Intelligence Magazine - May 2022 - 11

Yongduan Song, Chongqing University, CHINA
Jon Garibaldi, University of Nottingham, UK
Carlos A. Coello Coello, CINVESTAV-IPN, MEXICO
Georgios N. Yannakakis, University of Malta, MALTA
Huajin Tang, Zhejiang University, CHINA
Yew Soon Ong, Nanyang Technological University, SINGAPORE
Hussein Abbass, University of New South Wales, AUSTRALIA
Spotlight
Publication
CIS Publication Spotlight
IEEE Transactions on Neural
Networks and Learning Systems
Low-Cost Approximation-Based Adaptive
Tracking Control of Output-Constrained
Nonlinear System, by K. Zhao, Y. Song,
W. Meng, C. L. P. Chen, and L. Chen,
IEEE Transactions on Neural Networks
and Learning Systems, Vol. 32, No. 11,
November 2021, pp. 4890-4900.
Digital Object Identifier: 10.1109/
TNNLS.2020.3026078
" For pure-feedback nonlinear systems
under asymmetric output
constraint, we present a low-cost neuroadaptive
tracking control
solution
with salient features benefited from two
design steps. In the first step, a novel
output-dependent universal barrier
function (ODUBF) is constructed such
that not only the restrictive condition
on constraining boundaries/functions is
removed but also both constrained and
unconstrained cases can be handled
uniformly without the need for changing
the control structure. In the second
step, to reduce the computational burden
caused by the neural network
(NN)-based approximators, a single
parameter estimator is developed so that
the number of adaptive law is independent
of the system order and the
dimension of system parameters, making
the control design inexpensive in
computation. Furthermore, it is shown
that all signals in the closed-loop system
are semiglobally uniformly ultimately
Digital Object Identifier 10.1109/MCI.2022.3155328
Date of current version: 12 April 2022
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bounded, the tracking error converges
to an adjustable neighborhood of the
origin, and the violation of output constraint
is prevented. The effectiveness of
the proposed method can be validated
via numerical simulation. "
IEEE Transactions on
Fuzzy Systems
Concept Drift Detection: Dealing With
Missing Values via Fuzzy Distance
Estimations, by A. Liu, J. Lu, and G.
Zhang, IEEE Transactions on Fuzzy
Systems, Vol. 29, No. 11, November
2021, pp. 3219-3233.
Digital Object Identifier: 10.1109/
TFUZZ.2020.3016040
" In data streams, the data distribution
of arriving observations at different
time points may change-a
phenomenon called concept drift.
While detecting concept drift is a relatively
mature area of study, solutions to
the uncertainty introduced by observations
with missing values have only
been studied in isolation. No one has
yet explored whether or how these
solutions might impact drift detection
performance. We, however, believe
that data imputation methods may
actually increase uncertainty in the
data rather than reducing it. We also
conjecture that imputation can introduce
bias into the process of estimating
distribution changes during drift
detection, which can make it more difficult
to train a learning model. Our
idea is to focus on estimating the distance
between observations rather than
estimating the missing values, and to
define membership functions that allocate
observations to histogram bins
according to the estimation errors. Our
solution comprises a novel masked distance
learning (MDL) algorithm to
reduce the cumulative errors caused by
iteratively estimating each missing value
in an observation and a fuzzy-weighted
frequency (FWF) method for identifying
discrepancies in the data distribution.
The concept drift detection
algorithm proposed in this article is a
singular and unified algorithm that can
handle missing values, but not an
imputation algorithm combined with
a concept drift detection algorithm.
Experiments on both synthetic and
real-world datasets demonstrate the
advantages of this method and show its
robustness in detecting drift in data with
missing values. The results show that
compared to the best-performing algorithm
that handles imputation and drift
detection separately, MDL-FWF reduced
the average drift detection difference
from 10.75% to 5.83%. This is a nearly
46% improvement. These findings
reveal that missing values exert a profound
impact on concept drift detection,
but using fuzzy set theory to
model observations can produce more
reliable results than imputation. "
MAY 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 11
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IEEE Computational Intelligence Magazine - May 2022

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