1) dynamicpressure feedback network
动压反馈网络
2) feedback network
反馈网络
1.
A new method for designing feedback network;
一种新的数字反馈网络的设计法
2.
An equivalent circuit model of negative feedback network F is established toreplace its block diagram, so that it is convenient for its establishment and analytical calculation.
建立一个负反馈网络Ｆ的等效电路模型来替代Ｆ的方块图，解决了负反馈网络Ｆ的确定及定量分析计算的难点。
3.
Taking the negative feedback with voltage series as an example, the load effect of feedback network is analyzed quantitatively by employing the generalized h parameter of transistor.
以电压串联负反馈为例,采用晶体管的广义h参数,对反馈网络的负载效应做了定量分析。
3) Recurrent TSK
反馈TSK网络
4) dynamic recurrent neural network
动态反馈神经网络
1.
Model predictive control for complex systems based on dynamic recurrent neural network;
基于动态反馈神经网络的复杂系统预测控制
5) Dynamic pressure feedback
动压反馈
1.
Structure Design and Dynamic Simulation of Hydrokinetic Hammer with Dynamic Pressure Feedback Using Virtual Prototyping Technology;
基于虚拟样机技术的动压反馈式液动冲击器结构设计与动态仿真分析
2.
The concept of the dynamic pressure feedback was proposed to solve the strong hydraulic pressure shock in the system and tractor pitch oscillation.
对具有锥阀式结构的电液控制系统在操作平顺性、液压冲击、发热、卸荷压力、响应特性等方面进行了详细的研究,提出利用动压反馈装置解决冲击问题。
3.
The bond graph model is presented so as to simulate this kind of systems and improve accuracy of the simulation,which is meaningful to guide the design of this kind of servo systems while simulation shows that dynamic pressure feedback is a good means to compensate this system.
为此建立了该系统的键合图模型 ,利用该模型进行仿真 ,提高了仿真方法的准确性 ,仿真结果表明动压反馈是一种有效的补偿方法 ,提出的观点方法对此类伺服系统的设计具有积极的指导意
6) recurrent neural networks
反馈神经网络
1.
An alternative dynamic data rectification method based on recurrent neural networks(RNN)was studied in detail.
对基于反馈神经网络的化工过程动态数据校正方法进行了研究,实现了自反馈增益的网络结构和动态反向传播算法。
2.
The method of recurrent neural networks approximation is used in nonlinear discrete_time systems.
对反馈神经网络近似非线性离散系统的能力进行了扩展研究 ,针对于更普遍的非线性时变离散系统 ,证明了它们在有限时间段内的输出轨迹可以被反馈神经网络输出神经元的状态向量近似到任何程度。
3.
Starting from the universal approximation theorem of multilayered feed forward neural networks,this paper proves that the finite time trajectory of nonlinear continuous time system with input can be approximated by the state vector of the output units of a class of recurrent neural networks.
本文从多层前馈神经网络的一般近似定理出发 ,证明了带有输入的非线性连续系统在有限时间段内的输出轨迹可以被一类反馈神经网络输出神经元的状态向量近似到任何程度 。
补充资料：冲击波超压与动压