Neural Network System Design for Predicting MIN Reliability
S. Gupta1, Bindu Thakral2

1Shilpa Gupta*, Assistant Professor, Maharaja Agrasen University, Baddi, H.P, India.
2Dr. Bindu Thakral, Assistant Professor, Ansal University, Gurgao, Haryana, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 1216-1221 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5732059720/2020©BEIESP | DOI: 10.35940/ijitee.G5732.059720
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Abstract: Efforts have been made to examine and study different path and multi-path Multistage Interconnection Networks (MIN) possessing regular or irregular topology. Numerous strategies for establishing fault-tolerance in MINs have also been studied. These studies have provided us help to understand the strength and weakness of the existing static and dynamic and regular and irregular MINs. Application of Neural Networks leads to the development of MINs with improved performance and study of its Reliability In this paper ANN based system has been developed which will help in the study of metrics required for enhancing and predicting the reliability of MINs. In this paper Number of iterations are conducted to improve the ANN based system to predict the reliability of MINs by changing the number of neurons and the number of layers. 
Keywords: Multistage Interconnection Network(MIN), Artificial Neural Network (ANN), Mean time to failure (MTTF), Neurons, Layers.
Scope of the Article: Artificial Neural Network