Introduction to non-linear optimization
Introduction to non-linear optimization
Large-scale 0–1 fuzzy goal programming and its application to reliability optimization problem
Computers and Industrial Engineering
Fuzzy Sets and Systems - Special issue on nuclear engineering
Optimal system design considering maintenance and warranty
Computers and Operations Research
Form design of product image using grey relational analysis and neural network models
Computers and Operations Research
Decoupled control using neural network-based sliding-mode controller for nonlinear systems
Expert Systems with Applications: An International Journal
Bayesian reliability analysis for fuzzy lifetime data
Fuzzy Sets and Systems
Optimizing ballast design of wave energy converters using evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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This paper presents a novel continuous-state system model for optimal design of parallel-series systems when both cost and reliability are considered. The advantage of a continuous-state system model is that it represents realities more accurately than discrete-state system models. However, using conventional optimization algorithms to solve the optimal design problem for continuous-state systems becomes very complex. Under general cases, it is impossible to obtain an explicit expression of the objective function to be optimized. In this paper, we propose a neural network (NN) approach to approximate the objective function. Once the approximate optimization model is obtained with the NN approach, the subsequent optimization methods and procedures are the same and straightforward. A 2-stage example is given to compare the analytical approach with the proposed NN approach. A complicated 4-stage example is given to illustrate that it is easy to use the NN approach while it is too difficult to solve the problem analytically.