The structure of Electronic Voting Machine (EVM) is
an interconnected network of discrete components that record and
count the votes of voters. The EVM system consists of four main
subsystems which are Mother board of computer, Voting keys,
Database storage system, power supply (AC and DC) along with
various conditions of functioning as well as deficiency. The
deficiency or failure of system is due to its components
(hardware), software and human mismanagement. It is essential
to reduce complexity of interconnected components and increase
system reliability. Reliability analysis helps to identify technical
situations that may affect the system and to predict the life of the
system in future. The aim of this research paper is to analyze the
reliability parameters of an EVM system using one of the
approaches of computational intelligence, the neural network
(NN). The probabilistic equations of system states and other
reliability parameters are established for the proposed EVM
model using neural network approach. It is useful for predicting
various reliability parameters and improves the accuracy and
consistency of parameters. To guarantee the reliability of the
system, Back Propagation Neural Network (BPNN) architecture
is used to learn a mechanism that can update the weights which
produce optimal parameters values. Numerical examples are
considered to authenticate the results of reliability, unreliability
and profit function. To minimize the error and optimize the output
in the form of reliability using gradient descent method, authors
iterate repeatedly till the precision of 0.0001 error using MATLAB
code. These parameters are of immense help in real time
applications of Electronic Voting Machine during elections.
Assess Reliability Parameters of an Electronic Voting Machine using a Neural Network Technique
