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。
A mixed NN model is constructed using BP algorithm improved with dynamic learning rate.
利用动态学习率改进BP算法，建立了混合神经网络模型。
A classical Back Propagation Neural Network (BP NN) has been developed to solve the same problem for comparison.
并应用传统的BP神经网络解决同样的问题以进行比较。
To raise capacity that virtual surgical simulation system deals with real work, this article introduces BP NN model instead of finite element model, and realizes real-time biomechanical response.
为了提高虚拟手术仿真系统进行实际作业的能力，提出了以BP神经网络模型来代替有限元模型，实现实时的生物力学响应。
This paper uses GA-BP model to construct three-layer NN model.
本文应用GA-BP网络模型，建立了三层GA-BP神经网络模型。
So In this paper, it is mainly studied how to diagnose the fault in working process by means of torsional vibration of crankshaft combining with BP NN, Which belongs to The Second Diagnosis Class.
为此，本文研究如何利用神经网络诊断内燃机具体工作过程故障，属难度较大的二级诊断。
A collateral controller based on BP neural network (NN) and PID was designed for fatigue tester system.
针对疲劳试验机控制系统，设计了基于BP神经网络和PID的并行控制器。
Based on the research of the BP arithmetic and RBF arithmetic, the NN training and forecast model which is suitable for the environment-monitoring data is proposed.
通过对BP算法和RBF算法的研究，确定了适合环境监测数据COD值的神经网络训练与预测模型。
To further improve the forecast accuracy of this model, and reduce its forecast error, I also improve the BP NN in this article.
为了进一步提高模型预测的准确度，减小预测误差，本文还对BP神经网络进行改进。
Compared with BP NN, the model is more practical.
与BP神经网络相比，该模型更实用。
Compared with BP NN, the model is more practical.
与BP神经网络相比，该模型更实用。
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