A nonlinear system parameters identification problem is investigated in this paper by introducing a relaxation method used for solving the linear equations of the system.
研究了非线性参数系统模型的识别问题，通过引入求解线性方程组的松驰法思想，构造了一类新的迭代识别算法。
For a class of complex system with interconnected nonlinear subsystems, here is introduced an integrated identification method combining steady-state identification with dynamic identification.
针对一类具有相互关联非线性子系统的复杂系统，提出了一种稳态辨识与动态辨识相结合的集成辨识方法。
An online adaptive fuzzy neural network identification and robust control approach were proposed for the adaptive control problem of SISO nonlinear system.
针对单输入单输出非线性系统的自适应控制问题，提出了一种在线自适应模糊神经网络辨识与鲁棒控制的方法。
According to the selected scheme an equation is constituted for the observation. Identification algorithm for the nonlinear dynamic system, is deduced following the maximum likelihood principle.
然后根据选定的量测方案建立观测方程，并按极大似然原理导出非线性动力学系统的参数辨识算法。
An identification procedure of nonlinear unit feedback system is investigated by means of operator notation and Fourier transform, and some formulas for identification are given.
用算子符方法及傅里叶变换研究了单位反馈线性系统的一种辨识方法，并给出了辨识算式。
A combined iteration method for load identification is proposed to analyze the nonlinear dynamical system by joining finite element method with active control method.
针对一类非线性系统提出了一种新的载荷识别方法，组合迭代法。
A method of identification and state observer design of a nonlinear distributed parameter system is studied in this paper. A new approach for this kind of systems is proposed.
本文研究了一类非线性分布参数系统的模型辨识及其状态观测器的设计方法，为这类系统的研究提供了新的途径。
A new scaling kernel support vector regression was proposed for nonlinear system identification problem.
提出一种新的尺度核支持向量回归方法，并应用于非线性系统辨识问题。
Support Vector Regression (for short SVR) is an important branch of SVM, SVR has been applied to system identification, nonlinear system prediction and good results have been demonstrated.
支持向量函数回归(SVR)是SVM的一个重要分支，它已经成功地应用于系统识别、非线性系统的预测等方面，并取得了较好的效果。
Support Vector Regression (for short SVR) is an important branch of SVM, SVR has been applied to system identification, nonlinear system prediction and good results have been demonstrated.
支持向量函数回归(SVR)是SVM的一个重要分支，它已经成功地应用于系统识别、非线性系统的预测等方面，并取得了较好的效果。
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