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• W2893553662 abstract "The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches." @default.
• W2893553662 created "2018-10-05" @default.
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• W2893553662 date "2018-12-01" @default.
• W2893553662 modified "2023-09-29" @default.
• W2893553662 title "A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants" @default.
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• W2893553662 doi "https://doi.org/10.1016/j.neucom.2018.09.014" @default.
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