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A Case-based Reasoning with Feature Weights Derived by BP Network

机译:BP网络派生特征权重的基于案例的推理

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Case-based reasoning (CBR) is a methodology for problem solving and decision-making in complex and changing environments. This study investigates the performance of a hybrid case-based reasoning method that integrates a multi-layer BP neural network with
机译:基于案例的推理(CBR)是复杂和不断变化环境中的解决问题和决策的方法。本研究调查了一种基于混合案例的推理方法的性能,该方法与多层BP神经网络相结合

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