采用基于基本解方法和径向基函数插值的无网格算法(MFS- RBF)分析了广义的热弹性问题。
The method of fundamental solutions (MFS) with radial basis functions (RBF) approximation was developed for general thermoelastic analysis.
对于传统BP算法存在的收敛速度慢和易陷入局部极小值问题，人们提出了径向基函数网络。
People put forward radial basis function networks considering the conventional BP algorithm problems of slow convergence speed and easily getting into local dinky value.
结合改进的免疫算法和最小二乘法，提出了一种设计径向基函数(RBF)网络的两级学习方法。
A two-level learning method combining improved immune algorithm and least square method was proposed to design a radial basis function (RBF) network.
在RBF网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，使网络结构得到优化。
A learning algorithm of subtractive clustering for RBF network is used to obtain the parameters of radial basis function so as to optimize network structure.
在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，从而使神经网络结构得到优化。
A learning algorithm of subtractive clustering method for RBFNN is used to obtain the parameters of radial basis function, so that RBFNN has an optimized structure.
提出了一种交替梯度算法对径向基函数(RBF)神经网络的训练方法进行改进，并将之运用于电力系统短期负荷预测。
This paper proposes one kind of alternant gradient algorithm for improving the training of RBF neural network, which is applied to short-term electric load.
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。
One kind of alternant gradient algorithm for improving the training of Radial Basis Function (RBF) neural network is proposed.
针对模糊神经网络控制器难于设计的问题，提出了一种免疫进化算法用于径向基函数模糊神经网络控制器参数的优化设计。
Aiming at the design difficulty for fuzzy neural network controller, an immune evolutionary algorithm is proposed to design the parameters of a radial basis function fuzzy neural network controller.
本文对径向基函数网络提出了一种新的学习算法，利用最小均熵差准则对训练样本进行模式聚类。
This paper presents a new leaning method for radial basis function network, minimum mean entropy difference criterion algorithm is used to get pattern cluster of training sets.
把径向基函数和单元分解原理综合起来，提出一种大规模散乱点云的隐式曲面快速重构算法。
An efficient algorithm for implicit surface reconstruction for large-scale scattered data was proposed based on the combination of radial basis functions and partition of unity.
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。
Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is proposed.
采用一种基于免疫算法和最小二乘法的两级学习方法设计径向基函数（RBF）网络，并将其应用于雷达天线扫描方式识别系统。
A hybrid RBF training method based on immune algorithm and least square method is proposed and applied in radar antenna scanning-style recognition system.
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.
基于免疫进化算法，提出了一种设计径向基函数(RBF)网络的新算法——免疫径向基函数网络(IRBF)训练算法。
Based on immune evolutionary algorithm, a novel radial basis function (RBF) designing algorithm, the immune RBF (IRBF) network, was proposed.
该算法中，径向基函数神经网络(RBFNN)用作前向模型，IGLSA用于求解逆问题中的优化问题。
In the algorithm, the radial-basis function neural network (RBFNN) is utilized as forward model, and the IGLSA is used to solve the optimization problem in the inverse problem.
提出一种基于径向基函数(RBF)神经网络的一步超前预测控制算法。
In this paper, a one-step ahead predictive control algorithm based on Radial base Function (RBF) neural networks is proposed, which needs only one neural network.
接着分别针对径向基核函数和多项式核函数进行多次实验，分析这两种核函数对过滤算法的影响。
Then, experiments with the radial basic function and the polynomial kernel function are used to reveal the relationship between kernel functions and the filter algorithm.
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.
该模型首先采用改进的最近邻聚类算法确定径向基函数中心，接着应用递推最小二乘法训练网络的权值。
The model USES an improved nearest-neighbor clustering algorithm to select the RBF center, and a recursive least square algorithm to train weights of the RBF neural network.
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。
A 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。
A Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is studied.
针对R BF训练算法中径向基函数中心确定的困难，在分析比较目前较好的算法基础上，提出一种新的训练算法。
The major contributions of the dissertation are stated as follows: 1 a new training algorithm is proposed to obtain RBF centers.
针对R BF训练算法中径向基函数中心确定的困难，在分析比较目前较好的算法基础上，提出一种新的训练算法。
The major contributions of the dissertation are stated as follows: 1 a new training algorithm is proposed to obtain RBF centers.
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