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Neural-network models have a signifi- cant advantage over analytic models, though, because they require only failure hstory as input, no assumptions. Using that ...
It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as ...
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Oct 22, 2024 · This paper analyzes error distribution between the predictive value and the actual value with the application of Neural Network. Based on ...
May 9, 2024 · The increasing reliance on computer software has raised significant concerns regard- ing software reliability evaluation.
Results with actual testing and debugging data suggest that neural-network models are better at endpoint predictions than analytic models, and can be more ...
This paper proposes a deep neural network-based recommendation framework with prediction reliability. This framework filters out unreliable prediction ratings.
Using the failure history, the neural-network model automatically develops its own internal model of the failure process and predicts future failures. Because ...
The three steps of developing a neural network for reliability prediction are specifying a suitable network architecture, choosing the training data, and ...
2) A neural network model automatically develops an internal model of the failure process from the failure history and predicts future failures more accurately ...
This allows developers to accurately assess the current and future reliability of their products, estimate the man-months required for testing, and predict ...