该文提出一种用于复杂的非线性未知系统辨识的混合神经网络模型—自适应模糊神经网络(AFNN)。
This paper presents a compound neural network model, i. e., adaptive fuzzy neural network (AFNN), which can be used for identifying the complicated nonlinear system.
无模型控制方法非常适用于实际的阶数难以知道或难以辨识，且是时变的非线性系统。
The model-free control is especially useful for real nonlinear systems whose orders and modeling are very difficult to be known and time varying.
神经网络的非线性逼近能力的研究是神经网络成为辨识模型的理论基础。
The theory of identification model based on neural networks(NN)is to research into its capability of nonlinear approximation.
此无模型控制方法非常适用于实际的模型参数难以辨识，且是时变的非线性系统。
The model-free control is especially useful for real nonlinear systems whose model parameter are very difficult to be identified and time varying.
本文依据横摇力矩激振装置进行的船模激振横摇试验结果，提出了一套较完整的横摇非线性模型的辨识技术。
A comparatively complete technique to identify the rolling nonlinear model is developed according to the results of excited rolling tests of ship model using rolling moment exciter.
提出了应用近似非线性滤波技术，辨识陀螺漂移误差模型的方法。
This paper presents the application of an approximate nonlinear filter in the identification of gyro drift error model.
提出一种基于新的模糊模型和加权递推最小二乘算法(WRLSA)的非线性系统模糊辨识方法。
A fuzzy identification method for nonlinear systems is suggested based on a new fuzzy model and weighted recursive least square algorithm (WRLSA).
本文以小波网络和多模型理论为基础，对复杂非线性动态系统的辨识和控制方法进行了研究。
Based on wavelet networks and multiple model adaptive control theories, the identification and control methods for the complicated nonlinear dynamic systems are proposed.
RBF神经网络是一种三层前向网络，可有效用来进行非线性模型的辨识。
RBF neural network is a three-layer feedforward network and can be used to identify nonlinear model effectively.
针对现有的熔融碳酸盐燃料电池(MCFC)模型过于复杂的弊端，本文应用rbf神经网络辨识方法建立了MCFC的温度非线性模型。
According to the drawback of the models existed which are too complicated, we set up a nonlinear temperature model of MCFC using RBF neural networks identification technology.
由SVM辨识非线性系统的逆模型作为前馈控制器，形成直接逆控制。
SVM were used to identify the inverse model of nonlinear system, and this inverse model was used as feed-forward controller to design direct inverse control.
对非线性系统建立T-S模糊模型，并用正交最小二乘法（OLS）对模糊规则的后件参数进行辨识。
T S fuzzy model is constructed for nonlinear system in this paper, and orthogonal least squares (OLS) method is used to identify the parameters of fuzzy ruler consequents.
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 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.
利用该平台验证了提出的变负载直流电机双闭环调速系统的非线性状态空间模型及其参数辨识方法的有效性。
The platform verified that the proposed variable load DC Motor Speed Control System with nonlinear state space model and parameter identification method is effectiveness.
建立汽车发动机这样非线性系统的数学模型非常困难，人工神经网络理论为非线性系统的辨识提供了新的方法。
Mathematical model establishment in nonlinear system such as automobile engine is still very hard. Artificial neural network theory brings to us a new method in nonlinear system identification.
讨论了利用仅含一个隐层的前馈多层神经网络来辨识离散时间非线性动态系统时的模型检验问题。
This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.
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。
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.
提出了一种利用小波神经网络辨识非线性系统多模型故障的方法。
The method for the multiple model failure detection is presented based on wavelet neural network and the designed neural network observer to increase the precision of the identification.
高速公路交通流模型是一个高阶非线性时变系统，这使得该模型的辨识问题成为一个非常困难的问题。
The macro model of traffic flow in freeway is a high order, nonlinear and time variant system which makes the problem of its identification become very difficult.
辨识结果表明，动态递归网络模型优于传统辨识模型，适于非线性、不确定结构的辨识。
Results of identification show that the Elman's recurrent model is superior to the traditional model. It is adaptive to the identification of the non linear and uncertain structure.
与传统非线性辨识方法不同的是，神经网络辨识不受非线性模型的限制。
Different from the traditional nonlinear identification method, NNI is not restricted by the nonlinear model.
为了实现神经网络预测模型的鲁棒预测，提出一种基于非线性偏自相关的一般化预测模型辨识方法。
To solve the problem of the robust prediction of neural networks, the paper proposed a universal method of nonlinear model identification.
基于T - S模型，提出一种非线性系统的模型辨识方法。
A model identification approach of nonlinear systems is presented based on T-S model.
在模型辨识方面，我们专门研究了参数线性的非线性模型结构的选择技术及其算法。
On the aspect of model identification, we specially study the selection techniques for nonlinear model structure with linear parameters and the corresponding algorithms.
该模型用非线性刚度和非线性复合阻尼机理构造，模型中的参数由试验数据辨识。
The model is constructed by a nonlinear combination of dynamic nonlinear stiffness and dynamic nonlinear complex damping mechanisms.
该模型用非线性刚度和非线性复合阻尼机理构造，模型中的参数由试验数据辨识。
The model is constructed by a nonlinear combination of dynamic nonlinear stiffness and dynamic nonlinear complex damping mechanisms.
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