理论证明，只要神经网络辨识模型的精度足够高，就会获得很好的控制精度。
It is proved that the control performance is very well under the enough accuracy of the identification model.
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.
因此，此类神经网络非常适合于机器人运动学模型辨识及运动控制。
Therefore, the neural network is very suitable for the kinematic model identification and motion control of manipulator.
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.
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.
仿真结果表明采用RBF神经网络辨识建模的方法是有效的，建立的模型精度较高。
Simulation results indicate that the modeling method by using the RBF neural network identification technique is effective with the established model featuring a relative high precision.
对处于扰动状态下的预分馏塔的仿真结果表明，该算法可以有效地解决一类多神经网络模型的在线参数辨识问题。
Simulation results of a disturbed pre-fractionator show that this algorithm can be used to solve on-line parameters-recognized problem of a kind of multi- neural networks model effectively.
利用SHPB技术和自编的BP神经网络程序，以尼龙为代表性研究对象，对高聚物在高应变率下的本构模型进行了辨识。
Using SHPB technique and a BP neural network program, the constitutive model of polymers at high strain rates is identified.
然后介绍了如何使用模糊聚类算法和等价的前馈神经网络从样本数据中辨识离散的TS模型。
Then we introduce how to identify the TS model from sample data using fuzzy clustering algorithm and equivalent feedforward neural network.
分别采用最小二乘法和神经网络方法对上述方程进行求解，推导出了关节动态刚度和阻尼的辨识模型。
Joint dynamic stiffness and damping were identified via solving the former identification model by use of least square method (LSM) or neural network method.
针对控制模型的特点，利用最小二乘法和RBF神经网络构造了二次辨识的在线辨识算法。
An online identification algorithm was constructed using the least squares method and an RBF neuro network.
结果表明，用r BF神经网络辨识发动机起动模型，具有方法简单、学习速度快、辨识精度较高等优点。
The results show that this start model features simple procedure, quick to learn and precision when use RBF neural networks for engine model identification.
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.
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.
辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
通过神经网络对一实际的油气藏系统进行建模和辨识，从而由新的神经网络模型可以获得参数识别结果。
After a practice oil or gas reservoir is modeled and identified by a neural network, the results of parameter identification are obtained by the new neural network model.
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.
采用简化迟滞算子对模型进行预处理后，构造神经网络实现模型的辨识。
Then a neural network was built to identify the new model based on simplified hysteresis operators.
针对该天线展开过程的复杂性，利用三层BP神经网络对系统进行辨识，并建立了动态辨识模型。
Objecting on complexity for outspread process of HTDA, apply three-floor BP net to identifying system and set up identifiable dynamic model.
提出了一种基于CMAC神经网络控制系统，该系统由CMAC神经网络控制器和BP模型辨识网络组成。
This paper presents a neural network control system based on CMAC, which consists of a CMAC neural network controller and a BP network model identifier.
该控制器用具有改进学习算法的神经网络作pid参数调节器，用模糊神经网络对被控对象进行模型辨识。
In this controller, an improved study algorithm is adopted as the PID parameter regulator, and a fuzzy network is employed to identify the controlled objects.
结果表明，在无噪和有噪情况下，神经网络模型的辨识精度和泛化能力都要优于传统方法。
Compared with the classical method, the identification accuracy and the generalization capability of nn are testified to be superior in either the free - noise or noisy case.
仿真研究表明，只要恰当地选择神经网络正、逆模型的结构和辨识数据的长度等参数，实现加热炉神经网络内模自校正控制的结果是令人满意的。
Simulation shows that if chosen the appropriate ANN structure and training data quantity, its ANN internal model self-tuning control can be realized and the results can be acceptable.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
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.
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.
介绍了异步电动机矢量控制系统神经网络速度控制器的设计方法；同时提出了将开环直接计算与模型参考自适应方法相结合的神经网络混合转速辨识模型。
And a new hybrid speed identification method is also proposed, which based on the neural network, combines the open loop estimator and the instantaneous reactive power model reference adaptive system.
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.
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.
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