Computational Intelligence - November 2016 - 8

Publication
Spotlight

Haibo He, Chin-Teng Lin,
Kay Chen Tan, Graham
Kendall, and Yaochu Jin

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

Digital Object Identifier: 10.1109/
TNNLS.2016.2555845
"In this paper, the author presents a
novel computing model, called probe
machine (PM). Unlike the Turing machine (TM), PM is a fully parallel computing model in the sense that it can
simultaneously process multiple pairs of
data, rather than sequentially process every pair of linearly adjacent data. The author establishes the mathematical model
of PM as a nine-tuple consisting of data
library, probe library, data controller,
probe controller, probe operation, computing platform, detector, true solution
storage, and residue collector. The author analyzes the computation capability of the PM model, and in particular,
the author shows that TM is a special
case of PM. The author revisits two
NP-complete problems, i.e., the graph
coloring and Hamilton cycle problems,
and devises two algorithms on basis of
the established PM model, which can
enumerate all solutions to each of these
problems by only one probe operation.
Furthermore, the author shows that PM
can be implemented by leveraging the

Digital Object Identifier 10.1109/MCI.2016.2602070
Date of publication: 12 October 2016

Probe Machine, by J. Xu, IEEE Transactions on Neural Networks and Learning
Systems, Vol. 27, No. 7, July 2016, pp.
1405-1416.

nano-DNA probe technologies. The
computational power of an electronic
computer based on TM is known far
more than that of the human brain. A
question naturally arises: will a future
computer based on PM outperform the
human brain in more ways beyond the
computational power?"
Dynamic Energy Management System
for a Smart Microgrid, by G. K.Venayagamoorthy, R. K. Sharma, P. K. Gautam, and A. Ahmadi, IEEE
Transactions on Neural Networks and
Learning Systems, Vol. 27, No. 8,
August 2016, pp. 1643-1656.

adaptive dynamic programming and
reinforcement learning framework is
introduced for evolving the I-DEMS
online. The I-DEMS is an optimal or
near-optimal DEMS capable of performing grid-connected and islanded
microgrid operations. The primary
sources of energy are sustainable, green,
and environmentally friendly renewable energy systems (RESs), e.g., wind
and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage
and thermal generation were used to
overcome these challenges. Using the
I-DEMS to schedule dispatches allowed
the RESs and energy storage devices to
be utilized to their maximum in order
to supply the critical load at all times.
Based on the microgrid's system states,
the I-DEMS generates energy dispatch
control signals, while a forward-looking network evaluates the dispatched
control signals over time. Typical results
are presented for varying generation
and load profiles, and the performance
of I-DEMS is compared with that of
a decision tree approach-based DEMS
(D-DEMS). The robust performance of
the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions."
IEEE Transactions on Fuzzy Systems

Digital Object Identifier: 10.1109/
TNNLS.2016.2514358
"This paper presents the development of an intelligent dynamic energy management system (I-DEMS)
for a smart microgrid. An evolutionary

IEEE ComputatIonal IntEllIgEnCE magazInE | novEmbEr 2016

Ant-Inspired Fuzzily Deceptive Robots,
by M. Kouzehgar, M. Badamchizadeh
and M.-R. Feizi-Derakhshi, IEEE
Transactions on Fuzzy Systems, Vol. 24,
No. 2, April 2016, pp. 374-387.

Table of Contents for the Digital Edition of Computational Intelligence - November 2016