Research on offline identification of T-S model.
模糊模型离线辨识方法研究。
A model identification approach of nonlinear systems is presented based on T-S model.
基于T - S模型，提出一种非线性系统的模型辨识方法。
A design of T-S model based fuzzy controller for nonlinear system with uncertain time-delay was proposed.
讨论了一类由T - S模糊模型表示的不确定时滞非线性系统的模糊控制器设计问题。
The T-S model is used to extend the fully-decoupled parity equations to fuzzy parity equations for nonlinear systems.
引入T-S模型将全解耦奇偶方程推广到非线性系统中得到了模糊奇偶方程。
Robust model predictive control is proposed for a class of nonlinear systems with constraint inputs based on fuzzy T-S model.
针对一类基于模糊T-S模型的输入受限非线性系统提出了鲁棒模型预测控制。
A novel fuzzy logic controller based on T-S model is proposed for automatic generation control (AGC) system after deregulation.
本文针对解除管制后的自动发电控制系统(agc)提出了一种新颖的基于T - S模型的模糊逻辑控制器。
The problem of fuzzy modeling for a class of nonlinear time-delay systems with constraint inputs by fuzzy T-S model is addressed.
利用模糊T-S模型对一类输入受限的非线性时滞系统进行建模；
Based on fuzzy T-S model, a fuzzy method with self-learning capability for batch process modeling and optimal control is presented.
基于模糊t - S模型，提出一种具有自学习能力的模糊方法用于批过程建模和最优控制。
Moreover, T-S model is used to adjust the dynamic fuzzy rules by the latter neural network, which can improve the adaptability of the control system.
采用T - S模型，由后件网络动态调整模糊规则，提高控制系统的适应性。
The fuzzy T-S model is used to approximate the nonlinear systems, and the fuzzy control law of the fuzzy model is derived from the linear matrix inequality.
首先，采用模糊T-S模型来对非线性系统建模，由线性矩阵不等式得到模糊模型的控制律。
The paper introduces a kind of adaptive neural-fuzzy inference systems (ANFIS) based on T-S model to deal with the modeling problem of the complex soda carbonization process.
针对纯碱碳化过程的复杂建模问题，提出基于T-S模型的自适应神经模糊推理系统（ANFIS）的建模方法。
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.
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；
Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an adaptive control scheme for a class of nonlinear systems.
针对一类非线性系统，把模糊t - S模型和自适应模糊逻辑系统这两种模糊逻辑方式结合起来，提出了一种自适应控制方案。
Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an adaptive control scheme for a class of nonlinear systems.
针对一类复杂非线性系统，把模糊t - S模型和自适应模糊逻辑系统结合起来，提出了一种跟踪控制方案。
Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an observer-based tracking control scheme for a class of nonlinear systems.
针对一类非线性系统，把模糊t - S模型和自适应模糊逻辑系统两种模糊逻辑方式结合起来，提出了一种基于观测器的跟踪控制方案。
Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an observer-based tracking control scheme for a class of nonlinear systems.
针对一类复杂非线性系统，把模糊t - S模型和自适应模糊逻辑系统结合起来，提出了一种跟踪控制方案。
For nonlinear discrete systems, the T-S model is constructed and the parametric uncertainty and time-delay terms are introduced to make the fuzzy model approach to the original system more exactly.
针对非线性离散系统，构造t - S模型，引入参数不确定项和时滞项，使得模糊模型能够更精确逼近原系统。
Combining fuzzy Takagi-Sugeno (T-S) model with adaptive fuzzy logic systems, we present a tracking control scheme for a class of complex nonlinear systems.
针对一类非线性系统，把模糊t - S模型和自适应模糊逻辑系统这两种模糊逻辑方式结合起来，提出了一种自适应控制方案。
Combining both kinds of fuzzy logic forms including fuzzy t s model and adaptive fuzzy logic systems, this paper presents an observer based control scheme for a class of nonlinear systems.
针对一类非线性系统，把模糊t S模型和自适应模糊逻辑系统两类模糊逻辑方式结合起来，提出了一种基于观测器的控制方案。
In order to solve the difficult modeling problem of the lithopone calcination process, this paper proposes an adaptive neural fuzzy inference systems (ANFIS) modeling method based on t s model.
针对锌钡白干燥煅烧过程建模难的问题，提出了一种基于T S模型的自适应神经模糊推理系统(ANFIS)建模方法。
In view of modeling problems of nonlinear and dynamic system, a self organizing fuzzy identification algorithm (SOFIA) is presented based on t s model in this paper.
针对复杂非线性动态系统的模糊建模问题，基于T - S模型提出一种自组织模糊辨识算法。
A fuzzy neural network in t s model is constructed. Chaotic mechanism is introduced into the normal BP algorithm to train the weight parameters of the fuzzy neural networks.
出一种T - S模型的模糊神经网络，在通常BP算法的基础上，引进混沌机制来训练模糊神经网络的权值参数。
Combining fuzzy Takagi-Sugeno (T-S) model with adaptive fuzzy logic systems, we present a tracking control scheme for a class of complex nonlinear systems.
针对一类复杂非线性系统，把模糊t - S模型和自适应模糊逻辑系统结合起来，提出了一种跟踪控制方案。
Combining fuzzy Takagi-Sugeno (T-S) model with adaptive fuzzy logic systems, we present a tracking control scheme for a class of complex nonlinear systems.
针对一类复杂非线性系统，把模糊t - S模型和自适应模糊逻辑系统结合起来，提出了一种跟踪控制方案。
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