本文介绍了Matlab代码可用于一个版本，但不适用于另一个版本的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个可以在Matlab R2010a版本中运行的代码，但是在matlab R2008a中显示了错误.我正在尝试使用扩展的卡尔曼滤波器实现自组织模糊神经网络.我正在运行代码，但仅在matlab R2010a版本中有效.它不适用于其他版本.有什么帮助吗?
I have a code that works in Matlab version R2010a but shows errors in matlab R2008a. I am trying to implement a self organizing fuzzy neural network with extended kalman filter. I have the code running but it only works in matlab version R2010a. It doesn't work with other versions. Any help?
我收到的错误消息是在第49、130188和189行的']'处解析错误"
The error message that i am getting is "Parse error at ']' at lines 49, 130 188 & 189"
代码附加
function [ c, sigma , W_output ] = SOFNN( X, d, Kd )
%SOFNN Self-Organizing Fuzzy Neural Networks
%Input Parameters
% X(r,n) - rth traning data from nth observation
% d(n) - the desired output of the network (must be a row vector)
% Kd(r) - predefined distance threshold for the rth input
%Output Parameters
% c(IndexInputVariable,IndexNeuron)
% sigma(IndexInputVariable,IndexNeuron)
% W_output is a vector
%Setting up Parameters for SOFNN
SigmaZero=4;
delta=0.12;
threshold=0.1354;
k_sigma=1.12;
%For more accurate results uncomment the following
%format long;
%Implementation of a SOFNN model
[size_R,size_N]=size(X);
%size_R - the number of input variables
c=[];
sigma=[];
W_output=[];
u=0; % the number of neurons in the structure
Q=[];
O=[];
Psi=[];
for n=1:size_N
x=X(:,n);
if u==0 % No neuron in the structure?
c=x;
sigma=SigmaZero*ones(size_R,1);
u=1;
Psi=GetMePsi(X,c,sigma);
[Q,O] = UpdateStructure(X,Psi,d);
pT_n=GetMeGreatPsi(x,Psi(n,:))';
else
[Q,O,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n);
end;
KeepSpinning=true;
while KeepSpinning
%Calculate the error and if-part criteria
ae=abs(d(n)-pT_n*O); %approximation error
[phi,~]=GetMePhi(x,c,sigma);
[maxphi,maxindex]=max(phi); % maxindex refers to the neuron's index
if ae>delta
if maxphi<threshold
%enlarge width
[minsigma,minindex]=min(sigma(:,maxindex));
sigma(minindex,maxindex)=k_sigma*minsigma;
Psi=GetMePsi(X,c,sigma);
[Q,O] = UpdateStructure(X,Psi,d);
pT_n=GetMeGreatPsi(x,Psi(n,:))';
else
%Add a new neuron and update structure
ctemp=[];
sigmatemp=[];
dist=0;
for r=1:size_R
dist=abs(x(r)-c(r,1));
distIndex=1;
for j=2:u
if abs(x(r)-c(r,j))<dist
distIndex=j;
dist=abs(x(r)-c(r,j));
end;
end;
if dist<=Kd(r)
ctemp=[ctemp; c(r,distIndex)];
sigmatemp=[sigmatemp ; sigma(r,distIndex)];
else
ctemp=[ctemp; x(r)];
sigmatemp=[sigmatemp ; dist];
end;
end;
c=[c ctemp];
sigma=[sigma sigmatemp];
Psi=GetMePsi(X,c,sigma);
[Q,O] = UpdateStructure(X,Psi,d);
KeepSpinning=false;
u=u+1;
end;
else
if maxphi<threshold
%enlarge width
[minsigma,minindex]=min(sigma(:,maxindex));
sigma(minindex,maxindex)=k_sigma*minsigma;
Psi=GetMePsi(X,c,sigma);
[Q,O] = UpdateStructure(X,Psi,d);
pT_n=GetMeGreatPsi(x,Psi(n,:))';
else
%Do nothing and exit the while
KeepSpinning=false;
end;
end;
end;
end;
W_output=O;
end
function [Q_next, O_next,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n)
%O=O(t-1) O_next=O(t)
p_n=GetMeGreatPsi(X(:,n),Psi(n,:));
pT_n=p_n';
ee=abs(d(n)-pT_n*O); %|e(t)|
temp=1+pT_n*Q*p_n;
ae=abs(ee/temp);
if ee>=ae
L=Q*p_n*(temp)^(-1);
Q_next=(eye(length(Q))-L*pT_n)*Q;
O_next=O + L*ee;
else
Q_next=eye(length(Q))*Q;
O_next=O;
end;
end
function [ Q , O ] = UpdateStructure(X,Psi,d)
GreatPsiBig = GetMeGreatPsi(X,Psi);
%M=u*(r+1)
%n - the number of observations
[M,~]=size(GreatPsiBig);
%Others Ways of getting Q=[P^T(t)*P(t)]^-1
%**************************************************************************
%opts.SYM = true;
%Q = linsolve(GreatPsiBig*GreatPsiBig',eye(M),opts);
%
%Q = inv(GreatPsiBig*GreatPsiBig');
%Q = pinv(GreatPsiBig*GreatPsiBig');
%**************************************************************************
Y=GreatPsiBig\eye(M);
Q=GreatPsiBig'\Y;
O=Q*GreatPsiBig*d';
end
%This function works too with x
% (X=X and Psi is a Matrix) - Gets you the whole GreatPsi
% (X=x and Psi is the row related to x) - Gets you just the column related with the observation
function [GreatPsi] = GetMeGreatPsi(X,Psi)
%Psi - In a row you go through the neurons and in a column you go through number of
%observations **** Psi(#obs,IndexNeuron) ****
GreatPsi=[];
[N,U]=size(Psi);
for n=1:N
x=X(:,n);
GreatPsiCol=[];
for u=1:U
GreatPsiCol=[ GreatPsiCol ; Psi(n,u)*[1; x] ];
end;
GreatPsi=[GreatPsi GreatPsiCol];
end;
end
function [phi, SumPhi]=GetMePhi(x,c,sigma)
[r,u]=size(c);
%u - the number of neurons in the structure
%r - the number of input variables
phi=[];
SumPhi=0;
for j=1:u % moving through the neurons
S=0;
for i=1:r % moving through the input variables
S = S + ((x(i) - c(i,j))^2) / (2*sigma(i,j)^2);
end;
phi = [phi exp(-S)];
SumPhi = SumPhi + phi(j); %phi(u)=exp(-S)
end;
end
%This function works too with x, it will give you the row related to x
function [Psi] = GetMePsi(X,c,sigma)
[~,u]=size(c);
[~,size_N]=size(X);
%u - the number of neurons in the structure
%size_N - the number of observations
Psi=[];
for n=1:size_N
[phi, SumPhi]=GetMePhi(X(:,n),c,sigma);
PsiTemp=[];
for j=1:u
%PsiTemp is a row vector ex: [1 2 3]
PsiTemp(j)=phi(j)/SumPhi;
end;
Psi=[Psi; PsiTemp];
%Psi - In a row you go through the neurons and in a column you go through number of
%observations **** Psi(#obs,IndexNeuron) ****
end;
end
推荐答案
第49、130、188和190行粘贴在下面.注意到趋势了吗?
Lines 49, 130, 188, and 190 are pasted below. Notice the trend?
[phi,~]=GetMePhi(x,c,sigma);
[M,~]=size(GreatPsiBig);
[~,u]=size(c);
[~,size_N]=size(X)
从MATLAB 2009b开始，基本上开始使用了tilde运算符，因此以后不必使用的变量不必存储在内存中.这也意味着，其中带有波浪号的任何代码在该版本之前都不会向后兼容.
Basically as of MATLAB 2009b, the tilde operator was instituted so that variables that you didn't use later didn't have to be stored in memory. It also means that any code with tildes in it won't be backwards compatible before that version.
这篇关于Matlab代码可用于一个版本，但不适用于另一个版本的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！