模糊系统对非线性函数逼近能力的研究。
非线性函数逼近作为统计理论的一个重要分支，在模式识别中有着广泛的应用。
As one of the important branches in statistic theory, the non-linear function has a large application in model-identification.
因此依然有很多场合需要使用无模型方法—如用模糊系统进行非线性函数逼近等。
So there has been a great deal of interest in applying model-free methods such as fuzzy systems for nonlinear function approximation.
这种方法充分利用了神经网络的非线性函数逼近能力，构造神经网络自整定PID控制器。
Then the Neural Network PID control is realised in the model. This method makes full use of nonlinear function approximation of the Neural Network.
在此基础上，利用人工神经网络强大的非线性函数逼近能力，实现对建筑工程质量水平的评价。
On this basis, accomplishes the stage forecast for construction project quality evaluation based on the great nonlinear function approaching capability of the ANN.
对几种快速BP算法的特点及性能作了归纳和对比，并对一个非线性函数逼近实例进行了仿真研究。
In this paper, the characteristic and performance of various fast BP algorithms are generalized and contrasted through study on simulation of nonlinear function approximation experiment.
给水泵组机械状态信号具有非平稳性和非线性，而支持向量机具有良好的非线性函数逼近和泛化能力。
The mechanical state signal of boiler feed pumps is nonstationary and nonlinear, and support vector machines has an excellent nonlinearity approximation ability and better generalization capability.
分析了小波网络的性能，利用小波网络的非线性函数逼近能力，对非线性静态系统和非线性动态系统进行辨识。
The properties of the wavelet networks are analyzed. According to the approximation ability of wavelet networks, the nonlinear static system and the nonlinear dynamic system can be identified.
本文充分利用CMAC神经网络的非线性函数逼近功能，并结合电站数据采集和监测系统，提出一种校正电站测温传感器非线性输出特性的新方法。
This paper presents one new method of nonlinear calibration for temperature measurement sensors of power plant, which is based on CMAC neural network and data collecting and monitoring system.
利用BP网络可以逼近任意非线性函数的特点，对视觉系统进行标定，并使用MATLAB中的神经网络工具箱进行网络的设计和计算。
BP network can approach any nonlinear function. Using this characteristic, vision system was calibrated. The network is designed and calculated with MATLAB neural network toolbox.
其原因是神经网络具有其突出的优点：①能够逼近任意复杂的非线性函数关系；
The reason is that the neural network has its outstanding advantages: (1) It can approach any complicated nonlinear function relations;
近年来，各类模糊系统已被证明是万能函数逼近器，它们能实现任意的非线性连续控制规律和动态模型。
In recent years, fuzzy systems have been proved to be universal function approximators, which means they can realize any nonlinear continuous control laws and dynamic models.
最后将所提出的方法用于解决非线性函数的逼近问题。
Finally, the proposed method is applied to the problem of nonlinear function approximation.
介绍了径向基函数网络的函数逼近原理和方法，提出了一种基于广义回归神经网络（GRNN）的传感器非线性误差校正方法。
The RBF network function approximation theory and method are introduced, and the method of nonlinear error correction of sensor is presented based on generalized regression neural network(GRNN).
利用BP网络具有任意逼近非线性函数和内插值特性，提出一种实现常用热电阻阻值-温度变换的新方法。
Put forward a new approach which describe the relationship of resistance-temperature conversion for common thermo-resistance in terms of BP networks has the characteristic of non-linear.
利用模糊系统具有以任意精度逼近非线性函数的能力设计FDO对未知干扰和不确定进行估计，并通过鲁棒控制项来提高系统的性能。
Thanks to the approach ability of fuzzy systems, a FDO was used to estimate the unknown disturbances and uncertainties, and the whole system performance was enhanced by a robustifying item.
该算法利用RBF网络能以任意精度逼近任意函数的特性，模拟地表空间分布这一复杂的非线性函数。
Then, use the characteristic of RBF neural network can represent all functions at any accuracy to simulated surface of this complex spatial distribution function of the nonlinear.
本文将对一类非线性t - S模糊系统的非线性逼近能力进行研究，证明这种模糊系统在输入模糊子集为高斯型隶属函数的情况下，具有通用逼近性。
In this paper, we will study the nonlinear approximation of a kind of nonlinear T-S fuzzy system, and prove that when the fuzzy sets are Gaussian membership functions, it has universal approximation.
利用凝聚函数一致逼近非光滑极大值函数的性质，将非线性互补问题转化为参数化光滑方程组。
By using a smooth aggregate function to approximate the non-smooth max-type function, nonlinear complementarity problem can be treated as a family of parameterized smooth equations.
考虑到粘弹性材料阻尼性能随环境的非线性变化，运用GRNN(广义回归网络)对粘弹阻尼材料动态力学性能函数进行逼近，并构建预测模型。
Considering the non-linear behavior of the viscoelastic material according to the change of environment, the GRNN is used to make a model to predict the dynamic property of the material.
用高斯径向基函数(RBF)神经网络逼近对象未知非线性，用高增益观测器估计系统不可测量状态。
Gaussian based radial basis function (RBF) neural networks are used to approximate the plant's unknown nonlinearities, and a high-gain observer is used to estimate the unmeasured states of the system.
BP神经网络通过调节连接权重可以实现以任意精度逼近非线性函数，利用这一特点可对非线性函数关系进行拟合。
BP neural network can implement approximating nonlinear functions by arbitrary accuracy through regulating variable weight connection. This character can fit the nonlinear functions relations.
第二节介绍用三次矩阵样条函数方法逼近一阶矩阵非线性微分方程的数值解。
Section II describes the numerical solution of first-order matrix differential non-linear equation using the cubic matrix spline function.
一个简单的三层BP前馈网络，采用简单的非线性转移函数，就可以以任意精度逼近任何非线性函数。
A simple three-layer BP network using simple nonlinear transfer functions can approximate any nonlinear functions with any precision.
采用RBF神经网络逼近系统未知的非线性函数，引入滑模误差对其权值进行在线自适应调整，改善动态性能。
RBF neural network is proposed to approximate unknown nonlinear function. Sliding mode error is used to adaptively tune its weights online. Dynamics performance is improved.
提出非线性协同持续的概念，并提出用小波神经网络逼近非线性协同持续函数，同时证明沪深股市存在非线性协同持续关系。
So, this paper gives out the definition of the nonlinear common persistence. We make use of the wavelet neural network to approach the nonlinear common persistence...
提出非线性协同持续的概念，并提出用小波神经网络逼近非线性协同持续函数，同时证明沪深股市存在非线性协同持续关系。
So, this paper gives out the definition of the nonlinear common persistence. We make use of the wavelet neural network to approach the nonlinear common persistence...
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