Using neural network function approximation for optimal design of continuous-state parallel-series systems

DOI10.1016/S0305-0548(01)00100-9zbMath1029.90013OpenAlexW2159387524MaRDI QIDQ1869907

Ming J. Zuo, Max Q.-H. Meng, Peter Xiaoping Liu

Publication date: 28 April 2003

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0305-0548(01)00100-9

zbMATH Keywords

Neural networksmulti-state systemparallel-series systemcontinuous-state systemOptimal system design

Mathematics Subject Classification ID

Deterministic network models in operations research (90B10)

Related Items (7)

OPTIMAL DESIGN OF A MULTI-STATE WEIGHTED SERIES-PARALLEL SYSTEM USING PHYSICAL PROGRAMMING AND GENETIC ALGORITHMS ⋮ Form design of product image using grey relational analysis and neural network models ⋮ Fractional virus epidemic model on financial networks ⋮ Sequencing Optimization ink-out-of-nCold-Standby Systems Considering Mission Cost ⋮ Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment ⋮ Bayesian reliability analysis for fuzzy lifetime data ⋮ Service reliability modeling of distributed computing systems with virus epidemics

Uses Software

• Matlab

Cites Work

• Multivalued state component systems
• Optimal system design considering maintenance and warranty.
• Computer assisted reliability analysis: An application of possibilistic reliability theory to a subsystem of a nuclear power plant
• A decomposition for multistate monotone systems
• Coherent Systems with Multi-State Components
• Multistate reliability models
• Approximation by superpositions of a sigmoidal function

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