该文提出一个有效的基于径向基函数神经网络的模型和状态数据融合的汽轮发电机智能估计方法。
An efficient model based on radial basis function neural network and intelligent estimating method for data fusion of the turbine-generator is presented.
详细地介绍了径向基神经网络的结构和基本原理。
The structure and rational of RBF neural network are presented in detail.
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。
Presents a new hybrid framework of hidden Markov models (HMM) and radial basis function (RBF) neural networks for speech recognition.
最后对回归神经网络和径向基神经网络在故障诊断中的应用进行了初步的研究。
And, the application of Recurrent Network and Radius Basis Network to fault diagnosis is also primarily studied.
研究了车辆自动变速器的换挡建模问题，介绍了利用径向基神经网络（RBFN）的换挡建模方法和算法；
The gear shifting modeling problem for automated transmission is studied. The gear shift modeling method using radial basis function networks(RBFN) and arithmetic is introduced.
建立了基于相空间重构和径向基神经网络的压气机机匣静压的预测模型。
A novel forecasting model for compressor casing wall pressure based on phase space reconstruction and radial basis function network was established.
介绍了径向基函数(RBF)神经网络的结构和特点。
The structure and features of radial basis function (RBF) network are introduced.
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).
提出一种优化径向基函数神经网络来波方位(DOA)估计模型结构和参数的方法。
A novel algorithm for optimizing the structure and parameters of Direction of Arrival (DOA) estimation model based on radial basis function neural network is presented.
实验数据被用来训练径向基函数(RBF)神经网络，得出的神经网络结构和参数用于数据融合。
The experimental data are used to train a radial basis function (RBF) artificial neural network, and the construction and parameters of the network are obtained for data fusion.
针对PSD非线性对激光测平仪测量范围和测量精度的影响，采用一种新方法——径向基函数神经网络算法。
To eliminate the influence of nonlinear error of PSD on the measurement scale and accuracy of laser leveling tester, a new method of Radical Basis Function (RBF) neural network was used.
以某水面舰艇为研究对象，利用径向基函数神经网络算法和标况下的水池实验数据，建立了基于航速、航向角和海情自适应变化的船舶横向运动非线性参数模型。
An intelligent model for a ship's horizontal motion, which can self-adapt with navigating speed, ocean condition and course, was established based on the Radial Basis Function (RBF) neural network.
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。
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.
结果表明，径向基神经网络模型能有效提高预测精确度，也证明了实验方法的有效性和可行性。
The results not only show radial basis network models can increase the prediction accuracy efficiently, but also prove the validity and feasibility of these motheds.
利用径向基函数神经网络和选择的特征值对缺陷进行分类。
Defects are classified by radial basis function (RBF) network and features selected.
该方法通过选择径向基函数中心、确定神经网络隐层神经元的数目和调整每一层的权值和阈值，对由于PSD非线性产生的误差进行修正。
The nonlinear error of PSD was modified by choosing the centre of RBF, ascertaining the number of neural cell of the neural network and adjusting the weight and the threshold of each hiberarchy.
利用径向基人工神经网络（RBF）同时具有自组织神经网络和回归网络的优点，可以对缺失数据进行预测。
The RBF network possesses the advantages of Kohonen and regression networks. A test was performed to prove the effectiveness of RBF to complement the incomplete spatial information.
在径向基函数神经网络中，隐层中心的数量和位置的选择是整个网络性能优劣的关键，直接影响网络的分类能力。
The choice of quantity and position of hidden layer radial basis functions is very important and directly affects the goodness of fit of overall network classification ability.
结果表明，用径向基神经网络预测股价是可行的和有效的。
Results of prediction experiments with real data prove the efficiency of our prediction method based on Radial Basis Function neural network.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
用实际观测数据对该模型进行了试验，结果表明，用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。
The model was tested with observed data. The results showed that RBF Neural Network conversion accuracy than Quadratic fitting and BP Neural Network.
用实际观测数据对该模型进行了试验，结果表明，用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。
The model was tested with observed data. The results showed that RBF Neural Network conversion accuracy than Quadratic fitting and BP Neural Network.
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