An emotional learning-based fuzzy inference system for improvement of system reliability evaluation in redundancy allocation problem

A major challenge in reliability based optimal design problems is the evaluation of system reliability given a system design. Reliability evaluator is a tool, a model, or a system that analyses the reliability of a system given a specified system design. Reliability evaluator usually is embedded in reliability optimization model and it can be seen as a computational engine that can provide optimization model with the value of its objective function. Enhancing performance of the reliability evaluator in optimization models is important as the overall performance of optimization model is significantly affected by its embedded reliability evaluator. The purpose of this paper is to present an approach to improve the accuracy and generalization ability of evaluating system reliability in redundancy allocation problem. The main idea is to employ emotional learning-based fuzzy inference system (ELFIS) to improve performance of reliability evaluator. A series-parallel case reliability-redundancy allocation is considered and the proposed ELFIS is validated by comparison of its results with those of multi-layer perceptron neural network and adaptive network-based fuzzy inference system. Normalized mean squared error and mean absolute percentage error show that ELFIS can bring better accuracy in evaluating the system reliability for those reliability-based optimal designs.