A new method that determines radial basis function centers is proposed.
提出了一种新的确定径向基函数中心的方法。
Among them, the Radial Basis Function (RBF) neural network is a better model.
在这些预测模型中性能比较突出的是径向基函数网络(rbf)法。
The structure and features of radial basis function (RBF) network are introduced.
介绍了径向基函数(RBF)神经网络的结构和特点。
The structure and principle of Radial Basis Function (RBF) neural network are studied.
分析了径向基函数(RBF)网络的结构和工作原理。
The radial basis function network (RBFN) has good extensible and classified (ability).
径向基函数网络具有良好的推广能力和分类能力。
The triangle vector basis function (RWG) is employed to simulate the current distribution.
采用三角矢量面元(rwg)基函数，模拟电流分布。
An adaptive radial basis function neural network (ARBFNN) power control scheme is proposed.
提出了一种自适应r BF神经网络功率控制方案。
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。
A method based on radial basis function networks for forecasting chaotic time series is proposed.
给出了基于径向基函数网络的混沌时间序列预测的方法。
Rather, the coefficient ratios between certain basis function are kept fixed at predetermined value.
一定基函数之间的系数的比值保持固定在预定值。
The classification mechanism of a radial basis function network (RBFN) is investigated in this paper.
本文研究了径向基函数网络(RBFN)的分类机理问题。
A model of rough radial basis function (RBF) neural network with attribute significance is presented.
提出一种基于属性重要性的粗糙rbf神经网络模型。
It mainly consists of a thick tin oxide gas sensor array and radial basis function(RBF) neural network.
该电子鼻主要由一组厚膜金属氧化锡气体传感器阵列和RBF神经网络组成。
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。
Performance of radial basis function network highly depends on the locations of radial basis function centers.
径向基函数网络的性能在很大程度上取决于径基函数中心位置的选取。
Radial basis function (RBF) neural network is performed through linear combination of nonlinear basis function.
径向基函数(RBF)神经网络通过非线性基函数的线性组合执行。
Lastly, the optimized proportional navigation of modern fighter is researched with radial basis function (RBF).
最后，用径向基神经网络原理对载机导引器的优化作了研究。
Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is proposed.
提出了一种基于免疫识别原理的径向基函数神经网络学习算法。
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。
These meshless methods can be formulated in the general framework of radial basis function collocation techniques.
这些无网格方法可用径向基函数配置点方法这个框架统一描述。
A classification method based on fuzzy vector space model and radial basis function network is presented in this paper.
文本提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
One kind of alternant gradient algorithm for improving the training of Radial Basis Function (RBF) neural network is proposed.
提出一种交替梯度算法，对径向基函数(RBF)神经网络的训练进行改进。
A sine basis function model is introduced, bringing forward an example of Hilbert convertor and differentiator optimal design.
提出了一种正弦基函数神经网络模型，给出了海尔·伯特变换器与微分器优化设计实例。
Based on the Radial Basis Function neural network, a kind of fault auto-diagnosis system of dam safety monitoring is established.
基于径向基函数神经网络，建立了大坝安全自动化监测的非线性故障自诊断系统。
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。
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。
The prediction method of weight local basis function is presented based on the deep research on local prediction for chaotic time series.
在深入研究混沌时间序列局域预测方法的基础上，提出了一种加权局域基函数预测方法。
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。
When error goal is 0. 01 and speed constant of radial basis function is 4, the network achieves optimization, and the total correct rate is 96%.
结果表明当目标误差为0．01，径向基函数的分布常数为4时，网络达到最优化，总的正确识别率为96％。
A method based on radial basis function neural network (RBF) was presented, which could simplify data stream of automobile diagnosing instruments.
提出了一种用r BF网络(径向基函数网络)简化汽车故障诊断仪数据流功能的方法。
应用推荐