Diagonal recurrent neural network (DRNN) is a non-unity feedback network.
对角神经网络(DRNN)为非全反馈式动态神经网络。
A new blind equalization algorithm based on diagonal recurrent neural networks (DRNN) is proposed.
提出了一种基于对角递归神经网络的盲均衡算法。
In this paper, three layers of DRNN is used in the real - time identification and control of DC motor.
本文研究了三层对角回归神经网络(DRNN)用于直流电动机实时控制的方法。
The property of clustering learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning ability.
DRNN聚类学习的性能使得它非常适用于与在线学习方式相结合的实时语音识别系统。
Finally, we present a new control scheme of air pressure and flow and build PID decoupling control arithmetic based DRNN of air control system.
最后，提出了新的气源压力、流量系统控制方案，建立了对气源压缩空气压力、流量系统的基于神经网络整定的PID解耦控制算法。
DRNN is used to adjust the parameters of PID control on-line, accordingly it can make static and dynamic performance index comparatively ideal.
利用对角递归神经网络在线自适应调整PID控制器的参数，从而使系统的静态和动态性能指标较为理想。
The results of experiments show that AC servo system based on DRNN PID control has quick response speed, high steady accuracy and good robustness.
实验结果表明，基于对角递归神经网络整定的PID控制的交流伺服系统具有响应速度快、稳态精度高和鲁棒性强等特点。
According to the limited controllability of the multivariable coupling system, a PID self-tuning mixed decoupling control method based on DRNN is put forward.
针对可控受限多变量耦合系统，提出了一种基于对角递归神经网络(DRNN)整定的PID混合解耦控制。
This paper presents an adaptive congestion control model in ATM networks at the user to network interface by using a diagonal recurrent neural network (DRNN) as an predictor.
提出一种在用户-网络接口处利用对角递归神经网络(DRNN)作为自适应预测器，实现AT M网络自适应拥塞控制的模型。
Diagonal recurrent neural network (DRNN) is a modified model of the fully connected recurrent neural network with the advantage in capturing the dynamic behavior of a system.
对角循环神经网络是一类经过修正的全连接循环神经网络，在系统动态行为的俘获方面具有明显的优势。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
When DRNN predicts that the number of cells in buffer exceeds the threshold limit in the next time cycle, a control signal is generated by the controller to throttle arrival cell rate.
当DRNN预测下一时刻缓冲区中的信元数超过阈值时，控制器产生一个反馈控制信号减小信源进入网络的信元速率以避免拥塞发生。
The DRNN controller was constructed based on the hysteretic characteristics of the GMA, and on-line learned the inverse hysteresis model of the GMA by the feedback-error learning scheme.
DRNN控制器是根据GMA的滞回特性构造的，通过反馈误差学习方案在线学习GMA的逆滞回模型。
The DRNN controller was constructed based on the hysteretic characteristics of the GMA, and on-line learned the inverse hysteresis model of the GMA by the feedback-error learning scheme.
DRNN控制器是根据GMA的滞回特性构造的，通过反馈误差学习方案在线学习GMA的逆滞回模型。
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