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 thesis studies the generalized rough set models and proposes a multi-level rough set approximation model CBM-RS based on a covering of the universe.
作者研究了粗糙集扩展理论，提出了一种多层粗糙集模型CBM-RS。 该模型是一种基于覆盖的扩展的多层粗糙集模型。
DCWA also provides an approximation for the generalized closed world assumption (GCWA) and supports argumentation.
同时DCWA支持争论推理，为广义封闭世界假设提供了一种逼近。
However, the generalized moving least squares approximation makes require least squares approximation with regard to functional and its derivative value on all nodes.
而广义移动最小二乘近似要求近似函数及其导数在所有节点处的误差的平方和最小。
The problem of parameter inversion in inhomogeneous medium was studied and a generalized ray approximation method to inverse 2-d medium parameters was introduced in this paper.
对非均匀介质参数反演问题进行了研究，并提出了用于反演二维介质参数的广义射线近似方法。
The least squares solution of inverse problems of generalized skew symmetric matrices and It's optimal approximation problems are discussed.
讨论了线性流形上广义次对称矩阵反问题的最小二乘解及其逼近问题。
The optical properties and electrical structure of V in ZnS supercell have been computed by means of plane wave pseudo-potential method(PWP) with generalized gradient approximation(GGA).
运用密度泛函平面波赝势方法（PWP）和广义梯度近似（GGA），对替代式掺杂钒（V）的闪锌矿（ZnS）的超晶胞电子结构进行了计算。
In this paper, the characteristic and performance of various fast BP algorithms are generalized and contrasted through study on simulation of nonlinear function approximation experiment.
对几种快速BP算法的特点及性能作了归纳和对比，并对一个非线性函数逼近实例进行了仿真研究。
Based on the generalized moving least square method, a new Element-Free Galerkin (EFG) double-variable approximation is applied to dynamic characteristic calculation and analysis of Euler beam.
以广义移动最小二乘法为理论基础，将同时考虑挠度和转角双变量的无单元法运用于欧拉梁的动力特性计算与分析。
Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classifier is proposed.
然后，结合泛化回归神经网络，设计出一种分类器。
Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classifier is proposed.
然后，结合泛化回归神经网络，设计出一种分类器。
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