Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

Abstract : The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficiency of both approaches is presented in numerical example, advantages and disadvantages are discussed.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.344-353, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_34〉
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David Lehký, Ondřej Slowik, Drahomír Novák. Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.344-353, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_34〉. 〈hal-01391333〉

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