代码搜索:Normalized

找到约 4,216 项符合「Normalized」的源代码

代码结果 4,216
www.eeworm.com/read/231340/14238410

m shaft_gui.m

function fig = shaft_gui() % This is the machine-generated representation of a Handle Graphics object % and its children. Note that handle values may change when these objects % are re-created. Th
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m modeplot2.m

function modeplot2(FILE,arg1,arg2) % [phi,lambda,fb]=modeplot2(FILE,W1,W2,N,P5) % % % INPUTS: % FILE: input file, can be template.m (Script) or template.mtx (*.mat) % W: speed of rotatio
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m modeplot.m

function modeplot(FILE,arg1,arg2) % [phi,lambda,fb]=modeplot(FILE,W1,W2,N,P5) % % % INPUTS: % FILE: input file, can be template.m (Script) or template.mtx (*.mat) % W: speed of rotation
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m modeplot2f.m

function modeplot2f(FILE,arg1,arg2) % [phi,lambda,fb]=modeplot2f(FILE,W1,W2,N,P5) % % % INPUTS: % FILE: input file, can be template.m (Script) or template.mtx (*.mat) % W: speed of rotat
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m modeplot1.m

function modeplot1(FILE,arg1,arg2) % [phi,lambda,fb]=modeplot1(FILE,W1,W2,N,P5) % % % INPUTS: % FILE: input file, can be template.m (Script) or template.mtx (*.mat) % W: speed of rotatio
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m campbell.m

function campbell(FILE,P2,P3,P4,P5) % campbell(FILE) % % % campbell('template_File_Name'); % % By I. Bucher, % Rev. 2.02 Aug-98, adapted to rotfe2 % Rev. 2.1 Aug-99, accep
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m modeplot.1m.m

function modeplot1(FILE,arg1,arg2) % [phi,lambda,fb]=modeplot1(FILE,W1,W2,N,P5) % % % INPUTS: % FILE: input file, can be template.m (Script) or template.mtx (*.mat) % W: speed of rotatio
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m fig5_19.m

% % This routine plots the first four Chebyshev polynomials % in the normalized frequency range from -1 to 1 % % Copyright (c) 1999 by P.Bretchko and R.Ludwig % "RF Circuit Design: Theor
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m fig5_20.m

% % This routine generates graphs of the loss factor and % insertion loss of the lowpass Chebyshev filters % of order 1 through 4. % % Copyright (c) 1999 by P.Bretchko and R.Ludwig %
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m svmdemo.m

function result = svmdemo(action,hfigure,varargin) % SVMDEMO demo on the Support Vector Machines. % % SVMDEMO demonstrates use of the algorithms which % solves the SVM problem with the both linear (L