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.
论文中利用建立的物理模型对开关输出电脉冲的上时间，以及开关线性波形和非线性波形之间的延迟时间进行计算，结果与实验测量数据相吻合。
The risetime of electrical pulse and the delay-time between the linear model and nonlinear model are calculated in this paper, and the results match the experimental measure.
摘要针对一类能够由中立型变延迟非线性微分方程描述的神经网络模型，给出了全局渐近稳定的不依赖于时间延迟的充分条件。
A sufficient condition guaranteeing the global asymptotical stability of the equilibrium point is derived for a class of neural network models with variable delay and neutral type delay.
基于混沌动力系统的相空间延迟坐标重构，利用混沌序列固有的确定性和非线性，提出了用于混沌时间序列预测的一种少参数非线性自适应滤波预测模型。
Based on the deterministic and nonlinear characterization of the chaotic signals, a new reduced parameter nonlinear adaptive filter is proposed to make adaptive predictions of chaotic time series.
基于混沌动力系统的相空间延迟坐标重构，利用混沌序列固有的确定性和非线性，提出了用于混沌时间序列预测的一种少参数非线性自适应滤波预测模型。
Based on the deterministic and nonlinear characterization of the chaotic signals, a new reduced parameter nonlinear adaptive filter is proposed to make adaptive predictions of chaotic time series.
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