1) normalApproximation
范数逼近
2) C-norm approximation
C-范数逼近
3) criterion appro
规范逼近
4) Function approximation
函数逼近
1.
The function approximation ability comparison of two wavelet networks and their applications;
两种小波网络的函数逼近能力比较与应用
2.
Function approximation capabilities of intuitionistic fuzzy reasoning neural networks;
直觉模糊神经网络的函数逼近能力
3.
Function Approximation Study of General Fuzzy System;
模糊系统的函数逼近特性研究
5) approximation of function
函数逼近
1.
And as the approximation of function for example,approximating the emulation of the different BP neural Netwlrk with the figure of primary function,it compares the performance of different BP neural network,Advantages.
并以函数逼近为例,通过对不同的BP神经网络仿真与原函数图像的拟合,比较不同的BP神经网络的性能,验证新型BP网络的优势,得出如何根据对象选取神经网络的结论。
2.
This paper discusses differences and relations between Newton and Lagrange interpolation polynomial in approximation of function.
讨论了Newton及Lagrange插值多项式在函数逼近中的联系和区别。
3.
This paper discusses differences and relations between Taylor Polynomial and Newton interpolation polynomial in approximation of function.
讨论 Taylor多项式与 Newton插值多项式在函数逼近中的区别和联系。
6) Numerical Approximation
数值逼近
1.
The Inference of probabilistic model of geotechnical parameters based on optimal numerical approximation method;
基于最佳数值逼近法的岩土参数概率模型推断
2.
Construction of quasi-Shannon interval wavelet and application in numerical approximation;
拟Shannon区间小波的构造及其在数值逼近中的应用
3.
Hopf bifurcation in numerical approximation for sunflower equation using delay as parameter;
以滞量为参数的向日葵方程Hopf分支的数值逼近
补充资料：Luxemburg范数
Luxemburg范数
Luxemburg nonn
L峨曰血叱范数〔I一血叱~;J如盆c服6yP住肋p-Ma] 函数 ，‘x!.(M，一、{*:*>o，丁、(，一’x(:))‘:‘1}， G这里M(u)是关于正的u递增的偶凸函数， 怒“一’M(u)一忽u(M(u))一，一0，对“>0，M(“)>0，且G是R”中的有界集.此范数的性质曾由W.A.J.h以油比飞〔11作了研究.L~b鸣范数等价于O正ez范数(见0口厄空间(C旧允2 sP创芜))，且 I{x}I(，)簇1 lx}I，蕊2 11 x 11(、).如果函数M(u)和N(u)是互补(或互为对偶)的(见O市口类(Or比zc地”‘、则 ，，·，，(一sun{)·(!，，‘!，“!:，，，，，《一‘，}·如果z‘(t)是可测子集E CG的特征函数，则 !l:二11‘M、-一下尖二一. ““启”‘川M一’(l/n篮‘E)’
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条