A Monte Carlo sampling plan for estimating network reliability
Operations Research
Network reliability and algebraic structures
Network reliability and algebraic structures
Neural networks and the bias/variance dilemma
Neural Computation
Elements of artificial neural networks
Elements of artificial neural networks
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Estimation of all-terminal network reliability using an artificial neural network
Computers and Operations Research
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Neural Networks, Fuzzy Logic and Genetic Algorithms
Neural Networks, Fuzzy Logic and Genetic Algorithms
A Hierarchical Modeling and Analysis for Grid Service Reliability
IEEE Transactions on Computers
IEEE Transactions on Computers
IEEE Transactions on Software Engineering
Prediction of vehicle reliability performance using artificial neural networks
Expert Systems with Applications: An International Journal
Structural reliability analysis using Monte Carlo simulation and neural networks
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
A Heuristic Algorithm for Reliability Modeling and Analysis of Grid Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The objective of this paper is to present a novel method to achieve maximum reliability for fault tolerant optimal network design when network has variable size. Reliability calculation is most important and critical component when fault tolerant optimal network design is required. A network must be supplied with certain parameters that guarantee proper functionality and maintainability under worse situations. Many alternative methods for measuring reliability have been stated in literature for optimal network design. Most of these methods mentioned in literature for evaluating reliability may be analytical and simulation based. These methods provide significant way to compute reliability when network has limited size. Also, significant computational effort is required for growing variable sized networks. Therefore, a novel neural network method is presented to achieve significant high reliability for fault tolerant optimal network design in highly growing variable networks. This paper computes reliability with improved learning rate gradient descent based neural network method. The result shows that improved optimal network design with maximum reliability is achievable by novel neural network at manageable computational cost.