function [magout,phase,w] = bode(a,b,c,d,iu,w)
%BODE Bode frequency response of LTI models.
%
% BODE(SYS) draws the Bode plot of the LTI model SYS (created with
% either TF, ZPK, SS, or FRD). The frequency range and number of
% points are chosen automatically.
%
% BODE(SYS,{WMIN,WMAX}) draws the Bode plot for frequencies
% between WMIN and WMAX (in radians/second).
%
% BODE(SYS,W) uses the user-supplied vector W of frequencies, in
% radian/second, at which the Bode response is to be evaluated.
% See LOGSPACE to generate logarithmically spaced frequency vectors.
%
% BODE(SYS1,SYS2,...,W) graphs the Bode response of multiple LTI
% models SYS1,SYS2,... on a single plot. The frequency vector W
% is optional. You can specify a color, line style, and marker
% for each model, as in
% bode(sys1,'r',sys2,'y--',sys3,'gx').
%
% [MAG,PHASE] = BODE(SYS,W) and [MAG,PHASE,W] = BODE(SYS) return the
% response magnitudes and phases in degrees (along with the frequency
% vector W if unspecified). No plot is drawn on the screen.
% If SYS has NY outputs and NU inputs, MAG and PHASE are arrays of
% size [NY NU LENGTH(W)] where MAG(:,:,k) and PHASE(:,:,k) determine
% the response at the frequency W(k). To get the magnitudes in dB,
% type MAGDB = 20*log10(MAG).
%
% For discrete-time models with sample time Ts, BODE uses the
% transformation Z = exp(j*W*Ts) to map the unit circle to the
% real frequency axis. The frequency response is only plotted
% for frequencies smaller than the Nyquist frequency pi/Ts, and
% the default value 1 (second) is assumed when Ts is unspecified.
%
% See also BODEMAG, NICHOLS, NYQUIST, SIGMA, FREQRESP, LTIVIEW, LTIMODELS.
% Old help
%warning(['This calling syntax for ' mfilename ' will not be supported in the future.'])
%BODE Bode frequency response for continuous-time linear systems.
% BODE(A,B,C,D,IU) produces a Bode plot from the single input IU to
% all the outputs of the continuous state-space system (A,B,C,D).
% IU is an index into the inputs of the system and specifies which
% input to use for the Bode response. The frequency range and
% number of points are chosen automatically.
%
% BODE(NUM,DEN) produces the Bode plot for the polynomial transfer
% function G(s) = NUM(s)/DEN(s) where NUM and DEN contain the
% polynomial coefficients in descending powers of s.
%
% BODE(A,B,C,D,IU,W) or BODE(NUM,DEN,W) uses the user-supplied
% frequency vector W which must contain the frequencies, in
% radians/sec, at which the Bode response is to be evaluated. See
% LOGSPACE to generate logarithmically spaced frequency vectors.
% When invoked with left hand arguments,
% [MAG,PHASE,W] = BODE(A,B,C,D,...)
% [MAG,PHASE,W] = BODE(NUM,DEN,...)
% returns the frequency vector W and matrices MAG and PHASE (in
% degrees) with as many columns as outputs and length(W) rows. No
% plot is drawn on the screen.
%
% See also LOGSPACE, SEMILOGX, MARGIN, NICHOLS, and NYQUIST.
% J.N. Little 10-11-85
% Revised A.C.W.Grace 8-15-89, 2-4-91, 6-21-92
% Revised Clay M. Thompson 7-9-90
% Revised A.Potvin 10-1-94
% Copyright 1986-2001 The MathWorks, Inc.
% $Revision: 1.22 $ $Date: 2001/01/18 19:49:57 $
ni = nargin;
no = nargout;
% Check for demo and quick exit
if ni==0,
eval('exresp(''bode'')')
return
end
error(nargchk(2,6,ni));
% Determine which syntax is being used
switch ni
case 2
if size(a,1)>1,
% SIMO syntax
a = num2cell(a,2);
den = b;
b = cell(size(a,1),1);
b(:) = {den};
end
sys = tf(a,b);
w = [];
case 3
% Transfer function form with time vector
if size(a,1)>1,
% SIMO syntax
a = num2cell(a,2);
den = b;
b = cell(size(a,1),1);
b(:) = {den};
end
sys = tf(a,b);
w = c;
case 4
% State space system without iu or time vector
sys = ss(a,b,c,d);
w = [];
otherwise
% State space system, with iu but w/o time vector
if min(size(iu))>1,
error('IU must be a vector.');
elseif isempty(iu),
iu = 1:size(d,2);
end
sys = ss(a,b(:,iu),c,d(:,iu));
if ni<6,
w = [];
end
end
if no==0,
bode(sys,w)
else
[magout,phase,w] = bode(sys,w);
[Ny,Nu,lw] = size(magout);
magout = reshape(magout,[Ny*Nu lw]).';
phase = reshape(phase,[Ny*Nu lw]).';
end
% end bode
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MATLAB-Program.rar_matlab 模糊控制_模糊 matlab_模糊 神经_神经模糊控制_神经网络控制
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MATLAB是一款广泛应用于科学计算、数据分析以及工程设计的高级编程环境,尤其在处理数值计算、符号计算、数据可视化以及建模仿真方面具有显著优势。在工科领域,MATLAB是学习和实现各种控制理论,包括模糊控制和神经网络控制的重要工具。 模糊控制是一种基于模糊逻辑的控制方法,它借鉴了人类的模糊推理过程,能够处理不确定性和非线性问题。在"模糊控制"部分,你将了解到模糊系统的构建,包括定义模糊集、建立模糊规则以及实现模糊推理过程。这通常涉及输入变量的模糊化、模糊规则的组合以及输出变量的去模糊化。通过MATLAB,你可以构建和仿真模糊控制器,理解和优化其性能。 神经网络控制则是利用神经网络的自学习和泛化能力来实现控制系统的设计。在"神经网络"部分,你可能会接触到多层前馈网络、反向传播算法以及径向基函数网络等经典模型。MATLAB提供了神经网络工具箱,支持创建、训练和优化神经网络,用于系统辨识、预测和控制。 "基础部分"很可能是MATLAB的基本操作和控制理论的基础知识,涵盖了矩阵运算、函数定义、绘图技巧以及Simulink的使用。Simulink是MATLAB的一个扩展,用于建立动态系统的可视化模型,特别适合于控制系统的设计和仿真。 "www.pudn.com.txt"可能是一个链接或者说明文档,指引你获取更多的资源或者课程相关的信息。在学习过程中,阅读这些额外的资料可以帮助你深化理解,并且提供实践项目或示例代码。 这个MATLAB压缩包涵盖了从基础知识到高级应用的完整学习路径,对于想要掌握模糊控制和神经网络控制的工科学生来说是一份宝贵的资源。通过深入学习和实践,你可以熟练地运用MATLAB进行模糊系统和神经网络的建模、分析和控制设计,提升你的专业技能。
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MATLAB-Program.rar (50个子文件)
神经网络
F_appr.m 758B
Rbf.m 1KB
cha_Rec.m 3KB
模糊控制
tank_1.mdl 26KB
tipper.fis 633B
tank_plant.mdl 20KB
tank.fis 782B
tank_3.mdl 28KB
tank_4.mdl 32KB
tank_2.mdl 28KB
www.pudn.com.txt 218B
基础部分
m4_6_1.mdl 11KB
ex2_6.m 404B
ex2_3.m 375B
ex3_2.m 1KB
m4_7_1.mdl 8KB
bode.m 4KB
ex1_mesh.m 279B
m4_8_1.mdl 11KB
ex2_1.m 66B
m4_2_1.mdl 8KB
ex3_1.asv 434B
ex1_view.m 97B
m4_6_3.m 369B
ex1_cell.asv 15B
m4_7_3.mdl 10KB
wadd.m 68B
m4_6_4.mdl 12KB
m4_5_1.mdl 11KB
s_file.m 428B
ex3_1.m 560B
m4_6_2.mdl 11KB
m4_7_4.mdl 19KB
m4_6_5.m 372B
ex1_struc.m 279B
ex1_struc.asv 30B
m4_7_2.mdl 10KB
ex3_3.asv 766B
ex3_4.m 798B
ex3_3.m 766B
m4_5_2.m 342B
ex1_cell.m 430B
simuexam.mdl 7KB
ex3_4.asv 1KB
factor.m 94B
ex2_4.m 126B
ex1_plot3.m 79B
ex2_2.m 139B
ex2_5.m 159B
ex3_2.asv 1KB
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