外回路采用神经网络内模控制。
The outer loop uses neural network and internal model control.
本文研究了基于逆系统方法的神经网络内模控制。
In this paper, a neural network internal model control scheme was studied, which was based on the design of inverse system.
文章研究了混沌动力学系统的神经网络内模控制策略。
This paper proposes a new control method of chaotic dynamical systems.
针对一类开环稳定的非线性系统，提出了一种基于模糊神经网络的非线性内模控制方案。
An internal model control strategy is proposed to a class of open loop stable nonlinear systems described by fuzzy neural networks.
该文提出一种基于对角递归神经网络的内模控制系统，并以跳汰生产过程床层松散状况为对象进行了研究。
This paper proposes an internal model control system based on recurrent neural network, and considers jigger layer loose condition as research object.
仿真研究表明，只要恰当地选择神经网络正、逆模型的结构和辨识数据的长度等参数，实现加热炉神经网络内模自校正控制的结果是令人满意的。
Simulation shows that if chosen the appropriate ANN structure and training data quantity, its ANN internal model self-tuning control can be realized and the results can be acceptable.
文章用径向基神经网络设计内模控制系统。
This paper designs a internal model control system with radial basis function neural networks.
研究了专家PID控制、模糊自适应PID控制、基于RBF神经网络整定的PID控制、基于RBF神经网络的内模控制。
We study expert PID control, fuzzy adaptive PID control, RBF neural network PID control, internal control based on RBF neural networks.
文中研究基于径向基(RBF)神经网络算法的内模控制策略在苯乙烯本体聚合反应相对分子质量分布控制领域的应用。
A nonlinear internal model control (IMC) strategy based on radial basis function (RBF) network models was proposed for bulk polymerization of styrene.
利用神经网络对非线性系统的逼近能力，把内模控制推广到聚合反应过程质量指标控制这一非线性系统中。
Taking advantage of the neural network's approximate ability to any nonlinear system, the internal model control strategy was extended to the quality control of polymerization.
仿真结果表明，与PID控制器相比，神经网络近似内模控制器能较好地抑制系统模型不确定性和工作点变化的影响。
Simulation studies demonstrate that the AIMNC strategy exhibits better control performance and robustness to model uncertainties and variation of operation points than the PID controller.
仿真结果表明，与PID控制器相比，神经网络近似内模控制器能较好地抑制系统模型不确定性和工作点变化的影响。
Simulation studies demonstrate that the AIMNC strategy exhibits better control performance and robustness to model uncertainties and variation of operation points than the PID controller.
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