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- Neural Network Trace Editor Training. 神经网络道编辑训练。
- The neural network can preferably solve the non-linear problem. 利用神经网络建模可以较好地解决非线性问题。
- Wavelet-based and neural network papers from the Journal Network! 详细说明:基于小波和神经网络的论文,来自期刊网!
- The Research on the Chaotic Neural Network Encoding? 混沌神经网络编码研究?
- Convergent rate of certain neural network models s. N. 一类神经网络模型的平衡点的收敛速率。
- Neural Network Inverse Control of Two-motor Synchronous System P. 中国科学技术大学中国科学技术大学两电机同步系统的神经网络逆控制。
- And carry out a neural network deign and simulation with MATLAB. 并用MATLAB仿真的方法进行神经网络的设计和模拟。
- Martin Hagan, Howard Demuth and Mark Beale. Neural Network Design. 戴葵等译.;神经网络设计
- We use neural network because of its simpleness and accurateness. 使用神经网络来进行客户离网预警的优点在于其模型建立简单,识别精度高。
- The theory of Fuzzy ARTMAP(FAM) neural network was described. 介绍了fuzzy ARTMAP(FAM)神经网络的一般原理。
- The improved EBP neural network algorithm is used in this system. 提出了对传统EBP神经网络算法的改进方法。
- To deal with the deficiency of the self-learn method of heat transfer coefficient during plate laminar cooling process,a BP neural network was put forward to improve the self adapting system. 针对中厚板在轧后层流冷却过程中采用传统自学习方法修正计算对流换热系数存在的不足,提出利用BP神经网络对自适应系统进行改进。
- Implementation of Fuzzy Systems using Multi-layered Neural Network. 使用多层的类神经网路来实作模糊系统.
- The paper further investigates the neural network PID controller. 论文深入研究了神经网络PID控制器。
- Finally, this neural network calibrates a fact system. 并应用此训练的网络对一实际的温度采集系统进行校正。
- High-speed photopolarimeter based on a linear neural network[J]. 引用该论文 杜西亮;戴景民;徐仲辉.
- Neural Networks Horizon Picker Training. 神经网络层位拾取训练。
- Nonlinear ANC Based on RBF Neural Networks? 基于径向基神经网络的非线性自适应除噪
- Optimized BP neural network has the capability of expression nonlinearity and also has the self study and adaptive function, and thus, it can realize the best parameter combination of PID control. 优化BP神经网络是一种前向神经元网络,具有学习速率快、振荡小、精度高的优点,将其隐含层单元分别作为比例(P)、积分(I)、微分(D)单元,可以建立参数自学习的PID控制器。
- In order to improve the precision of the model, a new method using self adaption and artificial neural networks to predict rolling force was developed. 为了提高中厚板轧机轧制力的预报精度,采用轧制力模型自适应与人工神经元网络相结合的方法进行中厚板轧制力的在线预报。