ConclusionOrthogonal experimental results could be used to training BP-NN and the trained BP-NN could forecast the extraction results of active components in Chinese herb medicines.

结论可利用正交实验数据对BP网络进行训练，利用训练好的网络对中药药效物质基础的超临界萃取结果进行预测。

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The NN learning block, using BP network, is determined by comparing the simulation results.

神经网络学习模块采用BP网络，通过仿真分析确定了网络的结构。

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At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.

同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K - NN、BP神经网络和支持向量机分类器。

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