Based on the learning characteristic of neural network and the function approximation ability of the wavelet, a new self tuning control algorithm is presented.
依据小波的非线性逼近能力和神经网络的自学习特性，提出了一种基于小波神经网络模型的自校正控制算法。
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).
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 theoretical basis of ANN is function approximation, it USES a two - level feedforward neural network to approach arbitrary function to realize better power flow control.
径向基函数神经网络的理论基础是函数逼近，用一个两层的前向网络去逼近任意函数，以更好地进行潮流控制。
This paper deals with the computational model for fuzzy reasoning neural network and its function approximation capability.
研究了模糊推理神经网络计算模型及其连续函数逼近能力。
Due to its structural simplicity, the radial basis function (RBF) neural network has been widely used for approximation and classification.
径向基函数(RBF)神经网络因其结构简单而被广泛地用于非线性函数近似和数据分类。
This paper introduced a three layer BP neural network, and realized the approximation of a continuous function.
构造一个三层BP神经网络，实现了连续函数的逼近。
Then the Neural Network PID control is realised in the model. This method makes full use of nonlinear function approximation of the Neural Network.
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。
RBF neural network is a kind of local approximation neural networks. In theory, it can approximate any continuous function if there is enough neuron.
RBF神经网络是一种局部逼近的神经网络，理论上只要足够多的神经元，R BF神经网络可以任意精度逼近任意连续函数。
In this paper, the function approximation of Gelenbe Neural Network (GNN) is discussed and it is proved that GNN can approximate any G-type polynomial by using constructional method.
该文研究了G神经网络的函数映射能力，给出了前馈g神经网络映射任意G型多项式的构造性证明。
Car used to enhance learning (Q learning), using neural network Q function approximation.
小车采用加强学习(Qlearning)，采用神经网络对Q函数逼近。
For the problem that the input and output of real systems is a continuous process relative to time, this paper proposed a process neural network model for continuous function approximation.
针对实际系统的输入输出是与时间有关的连续过程，提出了一类用于连续过程逼近的过程神经元网络模型。
A new recurrent neural network based on B-spline function approximation is presented. The network can be easily trained and its training converges more quickly.
提出一种新的基于基本样条逼近的循环神经网络，该网络易于训练且收敛速度快。
A new recurrent neural network based on B-spline function approximation is presented. The network can be easily trained and its training converges more quickly.
提出一种新的基于基本样条逼近的循环神经网络，该网络易于训练且收敛速度快。
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