ARTICLE IN PRESS
Neurocomputing 72 (2009) 2833–2834
Contents lists available at ScienceDirect
Neurocomputing
0925-23
doi:10.1
journal homepage: www.elsevier.com/locate/neucom
Editorial
Recent developments in natural computation
The main objective of this special issue is to present readerswith the significantly extended and improved versions of severalhigh-quality articles presented at the Third InternationalConference on Natural Computation (ICNC’07). ICNC’07 was heldjointly with The Fourth International Conference on FuzzySystems and Knowledge Discovery (FSKD’07) from August 24 to27, 2007 in Haikou, Hainan, China. ICNC’07 featured the most up-to-date research results in computational paradigms inspiredfrom nature, including biological, ecological, linguistic, andphysical systems. It is an exciting and emerging interdisciplinaryarea in which a wide range of techniques and methods are beingstudied for dealing with large, complex, and dynamic problems. Itreceived 1752 submissions from 35 countries/regions. Afterrigorous reviews, 770 papers were published in the ICNC’07proceedings. The acceptance rate for publication was 44%.
Through a careful selection from the papers in the ICNC’07proceedings published by IEEE Press, authors of 18 papers wereinvited to submit extended versions to this special issue. Allauthors except one submitted their version to Elsevier EditorialSystem for Neurocomputing. Each paper had gone thoughreviewing by at least three reviewers. Based on reviewers’comments, authors made further revisions. We present 12 papersin this special issue.
The first paper entitled ‘‘Behavioral task processing forcognitive robots using artificial emotions’’ authored by EvrenDaglarli, Hakan Temeltas, and Murat Yesiloglu presents anartificial emotional-cognitive system-based autonomous robotcontrol architecture. There are three levels, namely, behavioralsystem level, behavioral selection level, and emotion–motivationmodule, in its general control architecture.
‘‘Binary classification using ensemble neural networks andinterval neutrosophic sets’’ by Pawalai Kraipeerapun and ChunChe Fung proposes two approaches for binary classification.The first one applies a single pair of neural networks while thesecond one uses an ensemble of pairs of neural networks forthe binary classification. Interval neutrosophic sets are usedin both methods in order to represent imperfection in theprediction.
‘‘Identification and control of nonlinear systems by a time-delay recurrent neural network’’ by Hongwei Ge, Wenli Du, FengQian, and Yanchun Liang introduces time-delay and recurrentmechanisms into a recurrent neural network. A dynamic recurrentback-propagation algorithm is developed for training the pro-posed neural network. Experiments show that the proposednetwork is very effective for identification and control fordynamic systems.
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‘‘A granular computing framework for self-organizing maps’’by Joseph Herbert and JingTao Yao presents a granular computingframework for growing hierarchical self-organizing maps. Thisframework allows for precise definitions of the relationshipsbetween feature maps at different levels of a hierarchy. Analgorithm for the construction, decomposition, and updating ofthe granule-based self-organizing map is also introduced.
‘‘Division-based rainfall-runoff simulations with BP neuralnetworks and Xinanjiang models’’ by Qin Ju, Zhongbo Yu,Zhenchun Hao, Gengxin Ou, and Dedong Liu is a more applica-tion-oriented article. Back-propagation neural networks are usedfor rainfall-runoff (flood and non-flood periods) simulations. Theresults are analyzed and compared with Xinanjiang model, adistributed, conceptual watershed model mainly dealing withrainfall-runoff processes.
The sixth paper, entitled ‘‘Denoising by using multineuralnetworks for medical X-ray imaging applications’’ authored byYeqiu Li, Jianmingm Lu, Ling Wang, and Yahagi Takashi proposesan adaptive multineural networks filter (MNNF) for digitalradiological image restoration and enhancement. The MNNF isused to remove noise from X-ray images in their experiments. Awide range or images, computed tomography (CT), magneticresonance image (MRI) and nuclear medicine (NM), are scheduledfor further exploration.
‘‘Design and implementation of an artificial neuromolecularchip and its applications to pattern classification problems’’ by Yo-Hsien Lin, Jong-Chen Chen, and Chao-Yi Huang presents theirwork on design and implementation of an artificial neuromole-cular (ANM) chip. The ANM chip is an evolvable hardwarearchitecture that combines intra- and interneuronal levels ofprocessing. It is suggested through experiments that the ANMchip is capable at pattern classification.
‘‘A self-organizing feature maps and data mining baseddecision support system for liability authentications of trafficcrashes’’ authored by Pei Liu reports the developments of adecision support/reference system for liability authentications oftwo-vehicle crashes based on generated self-organizing featuremaps and data mining models. It is shown that this system workswell even with small data sets.
‘‘Fault diagnosis of power electronic system based on faultgradation and neural network group’’ by Chengcai Ma, XiaodongGu, and Yuanyuan Wang proposes a fault diagnosis approach withfault gradation by using three sub-BP neural networks. Experi-mental results show that this approach increases the correctnessrate of the fault diagnosis significantly and it is very suitable foron-line complex systems.
ARTICLE IN PRESS
Editorial / Neurocomputing 72 (2009) 2833–28342834
‘‘Learning faces with the BIAS model: on the importance of thesizes and locations of fixation regions’’ by Predrag Neskovic, IanSherman, Liang Wu, and Leon Cooper presents a study on theBayesian integrate and shift (BIAS) model for learning objectcategories, and investigate its sensitivity to changes in the sizesand locations of fixation regions. Experimental results on a factcategory have been made.
‘‘Responding efficiently to relevant stimuli using an emotion-based agent architecture’’ by Rodrigo Ventura and CarlosPinto-Ferreira proposes an emotion-based agent system thatprovides adequate and efficient response to relevant stimuli fordecision making. It is biologically inspired by the emotionmechanisms found in the brain, following recent neurophysiolo-gical research.
The last paper, entitled ‘‘Variant of Gaussian kernel andparameter setting method for nonlinear SVM’’ by Shui-shengZhou, Hong-wei Liu, and Feng Ye, discusses the classificationproblem with a new nonlinear support vector machine. Inparticular, the Gaussian kernel is modified by analyzing itsperformance with stretching ratio. Both angle and distancecriteria are considered in order to find a better kernel parameter.
This special issue is a result of the dedication of the authors,the reviewers, and ICNC’07 participants. It was a pleasure for us towitness very critical and constructive discussions and reviews. Weare grateful for their contribution. We thank the Editor-in-Chief,
Dr. Tom Heskes, of Neurocomputing for his support andencouragement. We would also like to thank our colleagues ofthe ICNC’07 and FSKD’07 organizing team, especially Dr. LipoWang for providing such an excellent environment for researchpresentation and discussion.
JingTao Yao �
Department of Computer Science, University of Regina,
Regina, SK Canada S4S 0A2
E-mail address: [email protected]
Qingfu ZhangSchool of Computer Science and Electronic Engineering,
University of Essex, Wivenhoe Park,
Colchester CO4 3SQ, UK
E-mail address: [email protected]
Jingsheng LeiSchool of Information Technology, Hainan University,
Haikou 570228, China
E-mail address: [email protected]
Available online 6 April 2009
� Corresponding author. Tel.: +1306 585 4071.
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