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Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

机译:分类器集成增量学习程序,用于在不同运行条件下进行核瞬变识别

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摘要

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.
机译:对经验诊断系统进行实际实施的一个重要要求是能够在所有工厂运行条件下对瞬变进行分类。本文提出了一种基于分类器集合的方法,用于在不同操作条件下增量学习瞬态。在新运行条件下发生的瞬变没有得到令人满意的分类的情况下,将新的分类器添加到集合中。合奏的构造是通过套袋完成的;基本分类器是监督的模糊C均值(FCM)分类器,其结果通过多数表决进行合并。增量学习程序被应用于识别沸腾水堆(BWR)在不同反应堆功率水平下的给水系统中的模拟瞬变。

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