预测结果要优于BP人工神经网络（Back Propagation NeuralNetworks,BP-NN）.说明红外光谱技术能够实现奶粉脂肪含量的无损检测,检测过程比化学检测方法简单快速,操作性强.
基于2个网页-相关网页
Flexible BP NN 柔性BP神经网络
improved BP-NN model 改进的BP神经网络模型
G-BP-NN 遗传BP神经网络 ; 神经网络
GD-BP-NN 神经网络 ; 梯度下降BP神经网络
BR-BP-NN 神经网络
BP NN 前向反馈型神经网络
bp nn model bp神经网络模型
improved bp nn 改进bp网络
momentum bp-nn 动量bp神经网络
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。
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。
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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