Computational Intelligence - February 2015 - 16

Publication
Spotlight

Derong Liu, Chin-Teng Lin,
Kay Chen Tan, Graham Kendall,
and Angelo Cangelosi

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

An Incremental Design of Radial Basis
Function Networks, by H. Yu, P.D.
Reiner, T. Xie, T. Bartczak, and B.M.
Wilamowski, IEEE Transactions on
Neural Networks and Learning Systems,
Vol. 25, No. 10, October 2014, pp.
1793-1803.
Digital Object Identifier: 10.1109/
TNNLS.2013.2295813
"This paper proposes an offline algorithm for incrementally constructing and
training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit
is added to fit and then
eliminate the highest peak
(or lowest valley) in the
error surface. This process is
repeated until a desired
error level is reached.
Experimental results on real
world data sets show that
the ErrCor algor ithm
designs very compact RBF
networks compared with
the other exiting algorithms.
Several benchmark tests
such as the duplicate patterns test and the two spiral problem were
applied to show the robustness of the ErrCor algorithm.The proposed ErrCor algorithm generates very compact networks.
This compactness leads to greatly reduced
computation times of trained networks."
Digital Object Identifier 10.1109/MCI.2014.2369881
Date of publication: 14 January 2015

A New Learning Algorithm for a Fully
Connected Neuro-Fuzzy Inference System, by C.L.P. Chen, J. Wang, C.H.
Wang, and L. Chen, IEEE Transactions
on Neural Networks and Learning Systems, Vol. 25, No. 10, October 2014,
pp. 1741-1757.
Digital Object Identifier: 10.1109/
TNNLS.2014.2306915
"A traditional neuro-fuzzy system is
transformed into an equivalent fully connected three layer neural network (NN),
namely, the fully connected neurofuzzy inference systems (F-CONFIS).
The F-CONFIS differs from traditional
NNs by its dependent and repeated
weights between input and hidden layers
and can be considered as
the variation of a kind of
multilayer NN. Therefore,
an efficient learning algorithm for the F-CONFIS
to cope these repeated
weights is derived. Further more, a dynamic
learning rate is proposed
for neuro-fuzzy systems
via F-CONFIS where
both premise (hidden) and
© digital vision
consequent portions are
considered. Several simulation results indicate that the proposed
approach achieves much better accuracy
and fast convergence."
IEEE Transactions on Fuzzy Systems

A Fuzzy Measure Approach to Systems
Reliability Modeling, by R.R. Yager,
IEEE Transactions on Fuzzy Systems,

IEEE ComputatIonal IntEllIgEnCE magazInE | fEbruary 2015

Vol. 22, No. 5, October 2014,
pp. 1139-1150.
Digital Object Identifier: 10.1109/
TFUZZ.2013.2282170
"Engineering reliability has greatly
attracted attention in systems reliability
modeling, which focuses on predicting
and optimizing the proper functioning of
systems of interrelated components.
Propositional logic is often used to model
the structure and interrelationship
between the components of a system.
Probability theory plays a fundamental
role in providing distributions to help to
model performance features of the individual elements in a complex system.The
confluence of propositional logic and
probability theory is at the core of the
technologies used to study system's reliability. The purpose of this study is to
investigate some ideas from fuzzy measure theory and to verify these system
structure functions which can be modeled using a fuzzy measure. In addition,
this study describes how some concepts
from the perspective of fuzzy measure
theory can be used for the reliabilitymodeling framework offering by system
signatures to provide tools for being
capable of performing the reliability of
complex systems."
GT2FC: An Online Growing Interval
Type-2 Self-Learning Fuzzy Classifier, by
A. Bouchachia and C. Vanaret, IEEE
Transactions on Fuzzy Systems, Vol. 22,
No. 4, August 2014, pp. 999-1018.
Digital Object Identifier: 10.1109/
TFUZZ.2013.2279554

Table of Contents for the Digital Edition of Computational Intelligence - February 2015