Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear por plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear por plant malfunctions. The correct identification of unlabeled transients-or transients of the "don't-know" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes re used for finding the best (sensitive and robust) algorithms. Various neural network based models re successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear por plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions.
['Mark J. Embrechts', 'Sandor Benedek']
I am writing a research paper on a idea Hybrid identification of nuclear power plant transients with artificial neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Hybrid identification of nuclear power plant transients with artificial neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear por plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear por plant malfunctions. The correct identification of unlabeled transients-or transients of the \"don't-know\" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes re used for finding the best (sensitive and robust) algorithms. Various neural network based models re successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear por plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions."}]