Download Neural Networks Advances in neural networks research: An

Neural Networks 22 (2009) 489–490
Contents lists available at ScienceDirect
Neural Networks
journal homepage: www.elsevier.com/locate/neunet
2009 Special Issue
Advances in neural networks research: An introduction
Robert Kozma a,∗ , Steven Bressler b , Leonid Perlovsky c , Ganesh Kumar Venayagamoorthy d
a
The University of Memphis, FedEx Institute of Technology, Memphis, TN, USA
b
Florida Atlantic University, Boca Raton, FL, USA
c
Harvard University, Cambridge, MA, USA
d
Missouri Science and Technology University, MO, USA
article
Keywords:
Neuroscience
Cognition
Machine Learning
Hybrid Systems
Soft Computing
Dynamic Systems
Image Processing
Bioinformatics
Robotics
Power Systems
info
abstract
The present Special Issue ‘‘Advances in Neural Networks Research: IJCNN2009’’ provides a state-of-art
overview of the field of neural networks. It includes 39 papers from selected areas of the 2009 International Joint Conference on Neural Networks (IJCNN2009). IJCNN2009 took place on June 14-19, 2009
in Atlanta, Georgia, USA, and it represents an exemplary collaboration between the International Neural
Networks Society and the IEEE Computational Intelligence Society. Topics in this issue include neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, intelligent signal processing and pattern
recognition, bioinformatics and biomedicine, and engineering applications.
© 2009 Elsevier Ltd. All rights reserved.
Overview of Neural Networks Research at IJCNN2009
The International Joint Conference on Neural Networks –
IJCNN2009 – is the premier international conference on neural
networks theory, analysis, and a wide range of applications. The
conference theme in 2009 has been ‘‘The Century of Brain Computation — Neural Network Alliances with Cognitive Computing and
Intelligent Machine Embodiments.’’ Studies into higher cognition
and brain functions represent an ultimate frontier of scientific research. It was more than 50 years ago when John Von Neumann
introduced his groundbreaking work on self-organization in cellular automata and on the intimate relationship between computers
and the brain (VonNeumann, 1958). In the past decades, in particular since the rebirth and explosive development of the field in
the mid 80s, neural network approaches have demonstrated their
excellent potential to support fundamental theoretical and practical research towards new generations of artificial intelligence and
intelligent computing.
IJCNN2009 is a truly interdisciplinary event with a broad range
of contributions on recent advances in neural networks, including
neuroscience and cognitive science, computational intelligence
and machine learning, hybrid techniques, nonlinear dynamics
and chaos, various soft computing technologies, bioinformatics
and biomedicine, and engineering applications. The conference
program included 519 papers which appeared in the proceedings
∗
Corresponding author. Tel.: +1 901 678 2497; fax: +1 901 678 2480.
E-mail address: [email protected] (R. Kozma).
0893-6080/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.neunet.2009.07.008
of IJCNN2009, published by IEEE Press, Piscataway, NJ, USA. The
final program of the conference reflects the broad international
impact of neural network science with authors from 52 countries
and from six continents of the world. Following the traditions, the
Neural Networks journal has a Special Issue containing extended
versions of selected papers presented at IJCNN. This special issue
includes 39 papers from selected areas covering the IJCNN2009
topics. The articles of this Special Issue represent significantly
extended and revised versions of the short conference papers
published in the Proceedings of IJCNN2009. Each paper has been
thoroughly reviewed and consequently revised by the authors,
before final acceptance for publication in the Neural Networks
journal.
The Special Issue starts with papers on neuroscience and cognition, which provide important biological and cognitive inspiration
for neural network research. The papers in this section deal with
miscroscopic, mesoscopic and macroscopic aspects of neural systems and high-level cognitive functions, such as spatio-temporal
orientation, multisensory perception, emotions, and language. The
second section includes papers on Machine Learning (ML) techniques. Machine learning is traditionally a key component of artificial intelligence and it provides innovative ideas for learning and
adaptation utilized in neural network models and in novel memory
designs.
Soft computing and hybrid algorithms combine neural, fuzzy,
and evolutionary approaches to computational intelligence; recent developments include artificial immune systems and quantum computing. Hybrid methods significantly contribute to the
robustness of intelligent technologies and find their applications
490
R. Kozma et al. / Neural Networks 22 (2009) 489–490
in a wide range of practical areas as demonstrated in this Special Issue. Nonlinear systems theory and chaos computing are
important emergent methods injecting innovative ideas into the
dynamic development of neural networks. Topics of significance
in this area include stability of neural dynamic systems, the role
of nonconvergent and chaotic trajectories in robust system performance, the constructive role of noise in biologically-inspired neural approaches.
Intelligent signal processing is a major field where neural
networks have clearly demonstrated their advantages with respect
to traditional statistical techniques. Neural networks have been
very successful in solving difficult signal processing and pattern
recognition problems. The present Special Issue includes excellent
examples of innovations in this area, including image processing,
time series analysis and prediction. Bioinformatics and biomedical
applications are among the most successful areas demonstrating
the usefulness of intelligent technologies. With the rapid growth
of the health industry, there is a clear demand for intelligent
technologies, and our Special Issue shows excellent examples of
innovative solutions. Engineering applications of neural networks
include robotics and machine embodiments, adaptive control
of complex systems, including power systems, manufacturing,
transportation. Cutting-edge research presented in the last chapter
demonstrates the latest achievements in the industrial application
area.
Acknowledgements
The Guest Editors of this Special Issue, Robert Kozma, Steven
Bressler, Leonid Perlovsky, and Ganesh Kumar Venayagamoorthy
appreciate the cooperation of IJCNN organizers and reviewers, who
made possible to produce this timely volume. We believe that
this comprehensive collection of high-quality papers by leading
experts in the field will stimulate and promote progress of neural
networks, and will lead to future successes in neural networks
research.
References
Von Neumann, J. (1958). The computer and the brain. Yale University Press.