针对工业过程中普遍存在的时滞、非线性、对象参数时变等特性，提出了一种基于最优预测的神经元模糊自整定PID控制算法。
To the widely existed characteristics of time-delay, non-linear and timevarying of parameters in the industry process, an adaptive neuron-fuzzy PID controller based on optimal prediction is presented.
针对其存在非线性、参数时变和大延迟等难以控制的特性，提出基于T - S模糊模型的预测函数控制新方法。
As the nonlinearity, time-varying parameters and large lag make the control difficult, a predictive functional control method based on T-S (Takagi-Sugeno) fuzzy model is presented.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
This paper presents an adaptive fault prediction method based on strong tracking filter, which can predict faults in a class of nonlinear time varying systems.
给出了一个新的用于线性时变参数结构系统模态参数识别的基于固定长度平移窗投影估计的递推子空间方法。
A novel recursive subspace method is developed based on fixed length moving window (FLMW) projection approximation used for estimating the modal parameter of linear time-varying structural system.
针对汽车方向动力学控制存在的非线性和参数时变不确定性问题，提出了一种新的基于单神经元的汽车方向自适应pid控制算法。
In view of the nonlinearity and parameter time-varying uncertainty of vehicle dynamics, a novel algorithm, i. e. single neural adaptive PID control strategy, is propsed for vehicle direction control.
通过分析被控对象的特性，采用分段线性化的方法设计变参数PID控制器，进而给出基于T-S模型的模糊PID控制策略；
Variable parameter PID control for the controlled object is designed by the method of segment linearization. Furthermore, T-S model based fuzzy PID control strategy is put forward.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
Then two better methods that one of correction of model error and the other of nonlinear filter by Strong Tracking Filter were proposed.
基于人工神经网络的非线性映射特性，在三维有限元计算的基础上，结合大坝原型观测资料，提出了大坝参数时变规律的反演方法。
On the basis of the nonlinear characteristics of ANN and 3-D FEM computation, an inversion method for the time-varying regularity of dam parameters is presented with the observation data used.
基于人工神经网络的非线性映射特性，在三维有限元计算的基础上，结合大坝原型观测资料，提出了大坝参数时变规律的反演方法。
On the basis of the nonlinear characteristics of ANN and 3-D FEM computation, an inversion method for the time-varying regularity of dam parameters is presented with the observation data used.
应用推荐