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Sabri Arik Dr. Sabri Arik
From the (archived) Special Topic of Artificial Neural Networks

According to our December 2007 Special Topics analysis of research in artificial neural networks over the past decade, the scientist at #4 by total citations is Dr. Sabri Arik, with 25 papers cited a total of 675 times. He is also the top-ranking scientist in this topic in terms of citations per paper, with an average of 27 cites per paper.

In Essential Science IndicatorsSM from Thomson Scientific, Dr. Arik’s record includes 29 papers, the bulk of which are classified in the field of Engineering, cited a total of 825 times for the period between January 1, 1997 and October 31, 2007. Dr. Arik is an Associate Professor in the Department of Computer Engineering at Istanbul University in Turkey.

In this interview below, Dr. Arik talks with about his highly cited research.

  Please tell us a little about your research and educational background.

I received my Ph.D. in Electrical-Electronics and Information Engineering from the London South Bank University in 1997 in the area of Artificial Neural Networks. Between 1997 and 2003 I was an Assistant Professor at the Department of Electrical and Electronics Engineering at Istanbul University. In 2003, I was appointed Head of the Department of Computer Engineering for three years at the same university and I am still working in this Department as an Associate Professor.

In recognition of my research achievement in the area of artificial neural networks, I received the Distinguished Young Scientist Award from the Turkish Academy of Sciences in 2002 and the Junior Science Award 2005 from the Scientific and Technological Research Council of Turkey in 2005.

I am a member of the Technical Committee on Cellular Neural Networks and Array Computing (CNN & AC) of the IEEE Circuits and Systems Society, and also serving as an Associate Editor for Neurocomputing.

  What first interested you in artificial neural networks?

It all started by my joining Prof. Tavsanoglu’s Research Group at London South Bank University in 1994 to do my Ph.D. studies. I was asked to select a Ph.D. subject out of two possible topics. I made my choice for the one entitled "Stability of Neural Networks." This whole field fascinated me as I went along, and what most interested me has been the mathematical nature of the work.

  Your most-cited paper in our database is the 2000 IEEE Trans. Circuit Syst. article, "On the global asymptotic stability of delayed cellular neural networks." Would you please walk our readers through this paper—what were your goals, what did you find, etc.?

In 1988, Chua and Yang proposed cellular neural networks (CNNs), which constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. Templates are the key parameters to determine the stability properties of CNNs.

In our most-cited paper, by employing a more general type of Lyapunov functional and using the contradiction method, we have derived new sufficient conditions for the asymptotic convergence of the equilibrium point for CNNs. By exploiting these conditions, it is possible to design stable templates for CNNs.

In fact, the viability of CNN applications strongly depends on the design of stable templates. Furthermore, stable templates are also important for the implementation of CNNs in the Very-Large-Scale Integration (VLSI) technology. Our paper basically presented a general mathematical framework for the stability and design of CNN templates.

  Another of your highly cited papers is the 1998 IEEE Trans. Circuit Syst. article, "Equilibrium analysis of delayed CNN's." Would you please talk briefly about this paper?

When Chua and Yang introduced CNNs they proved that, for symmetric templates, a CNN would be completely stable. However, this proof was incomplete and contained some gaps. In 1997, Chua and Wu presented a more rigorous proof of complete stability of CNNs with symmetric templates. However, this new proof required the cell activation to be differentiable and strictly increasing, which contradicts the piecewise linear cell activation used in the original CNN model.

Therefore, the question of complete stability of CNNs with respect to piecewise linear activation function was to be addressed. In our paper, we presented the first mathematical criterion which enabled us to analyze the complete stability of CNNs with symmetric or non-symmetric templates with respect to piecewise linear activation function. We have also used this criterion to derive new sufficient conditions for complete stability of CNNs.

  Where have you taken your work since the publication of these papers?

We have extended our results to different models of neural networks such as Hopfiled neural networks, Cohen-Grossberg neural networks, and bidirectional associative neural networks. We have obtained new results for global convergence dynamics of these classes of neural networks. These results have also been highly cited in the recent years.

On the other hand, we have recently bought a CNN chip called a cellular neural networks universal machine (CNN-UM) within a project funded by the Scientific and Technological Research Council of Turkey. We are currently carrying out some image and signal processing applications of CNNs on the CNN-UM.

  Where do you see this field going in five to ten years?

Research in the area of artificial neural networks has rapidly increased in recent years. There are now many researchers investigating theoretical and application aspects of neural networks. Specifically, discovering new mathematical methodologies and implementing neural networks in the VLSI technology would have a significant impact on the future research on artificial neural networks.

  What should the "take-away lesson" about your work be for the general public?

Ultimately, the goal of research in this area is to establish the conditions under which an artificial neural network can solve some practical engineering problems such as image processing, video signal processing, nonlinear signal processing, modeling of biological systems and higher brain functions and pattern recognition. Our results might help to design neural networks that could solve such engineering problems.

Sabri Arik, Ph.D.
Department of Computer Engineering
Istanbul University
Istanbul, Turkey

Sabri Arik's most-cited paper with 153 cites to date:
Arik S, Tavsanoglu V, “On the global asymptotic stability of delayed cellular neural networks” IEEE Trans. Circuit Syst-I 47(4): 571-4, April 2000. Source: Essential Science IndicatorsSM from Thomson Scientific.

2008 : February 2008 - Author Commentaries : Sabri Arik Interview Regarding Artificial Neural Networks