Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 22, No. 4 (November 2009) 317-332   

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  ESTIMATION OF NETWORK RELIABILITY FOR A FULLY CONNECTED NETWORK WITH UNRELIABLE NODES AND UNRELIABLE EDGES USING NEURO OPTIMIZATION
 
 
D. Bhardwaj
Department of Computer Science, GLA Institute of Technology and Management
P.O. Box 281406, Mathura, India
bdiwakar_2000@yahoo.com

S.K. Jain and Manu Pratap Singh*
Department of Computer Science, ICIS, Dr. B.R. Ambedkar University
P.O. Box Agra-282002, Uttar Pradesh, India
sandeepzen@yahoo.co.in - manu_p_singh@hotmail.com

* Corresponding Author
 
 
( Received: September 18, 2008 – Accepted in Revised Form: February 19, 2009 )
 
 

Abstract    In this paper it is tried to estimate the reliability of a fully connected network of some unreliable nodes and unreliable connections (edges) between them. The proliferation of electronic messaging has been witnessed during the last few years. The acute problem of node failure and connection failure is frequently encountered in communication through various types of networks. We know that a network can be defined as an undirected graph N(V,E). It is believed that in a network the nodes as well as the connections can fail and hence can cause unsuccessful communication. So, it is important to estimate the network reliability to encounter the network failure. Various tools have been used to estimate the reliability of various types of networks. In this paper we are considering the approach of neuro optimization for estimating the network reliability. We use the simulation annealing to estimate the probabilities of various nodes in the network and Hopfield model to calculate the energies of these nodes at various thermal equilibriums. The state of the minimum energy represents the maximum reliability state of the network.

 

Keywords    Network Reliability, Neural Optimization, Simulated Annealing, Hopfield Model

 

چکیده    در اين مقاله تلاش شده تا قابليت اطمينان يک شبکه کاملاً پيوسته که شامل تعدادي گره غيرقابل اطمينان و اتصالات (لبه هاي) غيرقابل اطمينان بين اين گره ها مي باشد، تخمين زده شود. در سال هاي اخير، شاهد افزايش سريع ارسال پيام هاي الکترونيکي بوده ايم. درشبکه هاي مختلف، مشکل حاد افت گره ها و افت اتصالات به دفعات اتفاق افتاده است. مي دانيم که يک شبکه را مي توان به صورت نمودار غيرمستقيم N(V,E) تعريف کرد. تصور بر اين است که در يک شبکه، گره ها هم مانند اتصالات مي توانند دچار افت شوند و در نتيجه باعث ارتباط ناموفق گردند. لذا مهم است که قابليت اطمينان شبکه در مواجهه با افت شبکه تخمين زده شود. روش هاي مختلفي براي تخمين قابليت اطمينان انواع مختلف شبکه ها به کار برده شده است. در اين مقاله، ما رويکرد بهينه سازي عصبي را براي تخمين قابليت اطمينان شبکه به کار برده ايم. ما از شبيه سازي حرارتي براي تخمين احتمال انواع گره در شبکه استفاده کرده ايم و براي محاسبه انرژي اين گره ها در موازنه هاي حرارتي مختلف، مدل هوپ فيلد را به کار گرفته ايم. وضعيتِ داراي کمترين انرژي ممکن، بالاترين قابليت اطمينان شبکه را دارد.

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