Through constructing the RBF network approximation facility, this paper proposes a novel fault diagnosis structure and method applying RBF network to the fault diagnosis of drum level sensor.
本文将基于RBF网络的信息融合技术应用于水位传感器的故障诊断，通过构建高精度RBF 网络逼近器，提出了一种新的故障诊断结构和诊断方法。
The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.
给出了粗糙神经网络的拓朴结构和学习算法以及粗糙神经网络的逼近定理。
The stress field around the crack tip is simulated via the constructed Back-Propagation Neural Network (BPNN) whereby achieving the approximation of the stress field around the crack tip.
通过构造反向传播神经网络，对裂纹尖端的应力场进行模拟，进而实现对裂纹尖端应力场函数的逼近。
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。
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。
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。
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 structures approximation analysis technology is studied based on neural network.
对基于神经网络的结构近似分析技术进行了研究。
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。
Since the Q of the resonant network in often low, the analysis in the form of fundamental sinusoidal approximation is not accurate enough.
由于谐振网络的品质因数比较低，以往常角的基波近似分析法准确性差。
Based on gradient algorithm and the fundamental approximation of feedforward network, a new supervised comprehensive training mechanism is put forward.
基于梯度算法和前馈网络所具有的普遍近似性质，提出了一种新的监督型多目标系统化训练机制。
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。
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。
The network has the advantages of less hidden layers, simple operation and powerful function approximation capacity.
该网络具有隐层少，运算简单和函数逼近能力强的特点。
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。
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。
Analysis of reliability to the complex network is a NP-Hard problem. Searching for simple and accurate approximation algorithms has high application value.
大型复杂网络的可靠性分析都是NP难题，寻求计算简单、准确的近似算法更具应用价值。
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。
This paper introduces the radial basis function (RBF) network in the seismic data processing, and realizes the inserting data in seismic data processing with function approximation method.
该文将径向基函数网络引入地震数据处理中，实现了函数逼近法地震数据的插值处理，在实际地震数据处理中取得了较好的应用效果。
Nonlinear neural network (NN) control strategy, which was certified the high capacity of approximation, is adopted to control this typical nonlinear system.
本研究采用神经网络控制来解决这类非线性系统的控制问题。
Meanwhile both the mapping approximation ability and predication performance of the network are analyzed in details.
同时通过分析和实验说明网络具有较强的映射能力和预测性能。
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。
In this paper, the first order approximation theory of dynamic logging is obtained by using the geometrical factor theory in downhole condition and RC network theory.
本文用井条件下的几何因子理论和RC网络的单位脉冲响应给出了动态测井的一阶正演理论。
In this paper, the first order approximation theory of dynamic logging is obtained by using the geometrical factor theory in downhole condition and RC network theory.
本文用井条件下的几何因子理论和RC网络的单位脉冲响应给出了动态测井的一阶正演理论。
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