This paper designs a internal model control system with radial basis function neural networks.
文章用径向基神经网络设计内模控制系统。
Based on the radial basis function neural networks this paper has established a model of a catalytic hydrofining.
文章采用径向基函数神经元网络建立了加氢精制反应器数学模型。
Then the unite attitude reference system of the whole ship is built based on the information fusion on the radial basis function neural networks.
然后基于径向基函数神经网络的信息融合技术建立全舰统一姿态基准系统。
It can train polynomial function, neural networks, or radial basis function (RBF) classifiers.
它可以对多项式函数，神经网络，径向基函数进行训练。
Presents a new hybrid framework of hidden Markov models (HMM) and radial basis function (RBF) neural networks for speech recognition.
提出了一种隐马尔可夫模型（HMM）和径向基函数神经网络（RBF）相结合的语音识别新方法。
Radial Basis Function Neural Network is a kind of Neural Networks which have simple topological structure and clear learn procedure.
径向基函数神经网络是一种拓扑结构简单、学习过程透明的神经网络模型。
The modelling problem of multivariable system using radial basis function (RBF) neural networks is studied.
采用径向基函数（RBF）神经网络进行多变量系统的建模研究。
Of great interest, popular multilayer perceptron (MLP), radial basis function (RBF) and polynomial neural networks are the focus of the paper.
其中，对于多层感知器网络、径向基函数网络、多项式网络尤其关注。
A new type of non linear self repairing control strategy based on model following method using radial basis function (RBF) neural networks is presented.
提出一种基于径向基函数(RBF)神经网络的模型跟随非线性自修复控制方法。
This paper proposes an efficient on-line learning method for radial basis function (RBF) neural networks.
本文提出了一种径向基函数神经网络的有效在线学习方法。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
This paper presents an adaptive algorithm of optimally determining the structures, number, positions and widths of kernel functions of the improved radial basis function (IRBF) neural networks.
本文提出了改进的RBF神经网络结构、核函数个数、位置与宽度优化算法。
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.
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.
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。
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