MATLAB: Have a issue with scalar

fuzzy logicFuzzy Logic Toolbox

i am having a issue with the plot mf function within creating my fuzzy ifnerence system when trying to plot the mf using the graph function i recieve the error expected input index to be scalar with value <=2 and or <=1 this can be seen below in my code generation

> fis = mamfis('Name', "Diabetes");

fis = addInput(fis,[0 30], 'Name',"Glucose");

fis = addmf(fis,"Glucose","trimf",[0 5 10],'Name',"low");

fis = addmf(fis,"Glucose","trimf",[10 15 20],'Name',"medium");

fis = addmf(fis,"Glucose","trimf",[20 25 30],'Name',"High");

fis = addInput(fis,[0 30],'Name',"BMI");

fis = addmf(fis,"BMI","trimf",[0 5 10],'Name',"low");

fis = addmf(fis,"BMI","trimf",[10 15 20],'Name',"medium");

fis = addmf(fis,"BMI","trimf",[20 25 30],'Name',"High");

fis = addOutput(fis,[0 1],'Name',"Diabetes");

fis = addmf(fis,"Diabetes","trimf",[0 0.2 0.4],'Name',"NoDiabetes");

fis = addmf(fis,"Diabetes","trimf",[0.5 0.6 0.7],'Name',"HasDiabetes");

RuleList = [1 2 1 1 1;

2 1 1 1 1;

3 3 2 1 1;

]

fis = addRule(fis,RuleList)

RuleList =

1 2 1 1 1

2 1 1 1 1

3 3 2 1 1

fis =

mamfis with properties:

Name: "Diabetes"

AndMethod: "min"

OrMethod: "max"

ImplicationMethod: "min"

AggregationMethod: "max"

DefuzzificationMethod: "centroid"

Inputs: [1×2 fisvar]

Outputs: [1×1 fisvar]

Rules: [1×3 fisrule]

DisableStructuralChecks: 0

See 'getTunableSettings' method for parameter optimization.

>> plotmf(fis,'input',1)

>> plotmf(fis,'input',2)

>> plotmf(fis,'input',3)

Error using plotmf (line 52)

Expected input index to be a scalar with value <= 2.

>> plotmf(fis,'output',2)

Error using plotmf (line 52)

Expected output index to be a scalar with value <= 1.

And when i use the code that the error generates i am not sure what to correct as i am very new to using MATLAB and gogoling the errors didnt seem to work

function varargout = plotmf(fis,varType,varIndex,numPts)

%PLOTMF Display all of the membership functions for a given variable.

% PLOTMF(fismat,varType,varIndex) plots all of the membership functions

% in the FIS called fismat associated with a given variable whose type

% (input or output) and index are respectively given by varType and

% varIndex. This function can also be used with the MATLAB function

% subplot.

% [xOut,yOut] = PLOTMF(fismat,varType,varIndex) returns the x and y data

% points associated with the membership functions without plotting them.

% [xOut,yOutUpper,yOutLower] = PLOTMF(fismat,varType,varIndex) returns

% the x and y data points associated with the upper and lower membership

% functions of a type-2 FIS called fismat without plotting them.

% PLOTMF(fismat,varType,varIndex,numPts) generates the same plot with

% exactly numPts points plotted along the curve.

% The varType input argument is specified by a character row vector or

% string scalar.

% For example:

% fis = mamfis;

% fis = addInput(fis,[0 10],'Name','service');

% fis = addMF(fis,'service','gaussmf',[1.5 0],'Name','poor');

% fis = addMF(fis,'service','gaussmf',[1.5 5],'Name','good');

% fis = addMF(fis,'service','gaussmf',[1.5 10],'Name','excellent');

% plotmf(fis,'input',1)

% See also EVALMF, PLOTFIS.

%====================================

narginchk(3,4)

fuzzy.internal.utility.validateFIS(fis)

if ~isa(fis,'FuzzyInferenceSystem')

warning(message('fuzzy:general:warnDeprecation_FISStructure'))

fis = convertfis(fis);

end

fuzzy.internal.utility.validCharOrString('Variable type',varType);

if varType~="input" && varType~="output"

error(message('fuzzy:general:errVariable_InvalidType'));

end

if varType=="output" && fis.type=="sugeno"

error(message('fuzzy:general:errPlotmf_SugenoOutputMF'));

end

validateattributes(…

varIndex, …

{'numeric'}, …

{'nonempty','scalar','real','positive','integer', …

'<=',length(fis.(varType))}, …

'', …

[char(varType) ' index'] …

)

if nargin<4

numPts = 181;

else

validateattributes(…

numPts, …

{'numeric'}, …

{'nonempty','scalar','real','finite','integer','>',1}, …

'', …

['number of points taken in ' char(varType) ' range'] …

)

end

var = fis.(varType)(varIndex);

numMFs = length(var.mf);

if numMFs == 0

error(message("fuzzy:general:errFIS_MissingMFs",varType))

end

x = linspace(var.range(1),var.range(2),numPts)';

if nargout<1

plot(var.mf,x,'XLabel',var.Name)

else

varargout{1} = x(:,ones(numMFs,1));

if nargout > 1

[varargout{2:nargout}] = getMembershipValue(var.mf,x);

if numMFs > 1

for i = 2:nargout

varargout{i} = varargout{i}';

end

if anyone can help it would be much appreciated.

Best Answer

Hi,

I assume you are trying to use the plotmf function in your fuzzy inference system.From the mamfis properties of your code,it is observed that there are 2 inputs(Glucose and BMI i suppose) and 1 output(Diabetes).

plotmf(fis,variableType,variableIndex)

In the plotmf function ,depending on the variableType(which is input or output), it is expected that variableIndex should be less than or equal to the number of input /output variables in fis.

plotmf(fis,'input',3)
plotmf(fis,'output',2)

In your code , since there are only 2 inputs and 1 output ,using the above lines throws an error.Please refer to the following documentation for more insight.

https://www.mathworks.com/help/fuzzy/plotmf.html#d120e35882

Hope this helps!

Related Solutions

MATLAB: Error with Fuzzy Tree Model

There were a couple of issues that needed fixing.

Every FIS needs to have its Inputs, Outputs and MFs defined separately.
FIS3 would not have two MFs at its inputs. All it gets from FIS1 or FIS2 is a number. It decides what that number means based on the MF defined on its (FIS3's) inputs. So, yes, in your case you would have to redefine FIS1 and FIS2 output MFs as the MFs for the two inputs to FIS3.
The error that you see - “Error using FuzzyInferenceSystem/addRule (line 1148). Invalid input membership function name in rule description.” is because the inputs of FIS3 are instantiated, by default, to have 3 MFs called 'mf1', 'mf2', 'mf3' for both the inputs. You can see this by typing the following in the command window after executing the script until the line that errors out:

fis3.Inputs(1).MembershipFunctions
fis3.Inputs(2).MembershipFunctions

They have these default member functions because fis3 was created in your script with the following command:

fis3 = mamfis('Name','fis3','NumInputs',2,'NumOutputs',1);

Here's the code with a couple of changes. You should be able to adapt this for your use:

% Fuzzy model 1
fis1 = mamfis('Name',"tipper1");
fis1 = addInput(fis1,[0 10],'Name',"service");
fis1 = addMF(fis1,"service","gaussmf",[1.5 0],'Name',"poor");
fis1 = addMF(fis1,"service","gaussmf",[1.5 5],'Name',"good");
fis1 = addMF(fis1,"service","gaussmf",[1.5 10],'Name',"excellent");
fis1 = addInput(fis1,[0 10],'Name',"food");
fis1 = addMF(fis1,"food","trapmf",[-2 0 1 3],'Name',"rancid");
fis1 = addMF(fis1,"food","trapmf",[7 9 10 12],'Name',"delicious");
fis1 = addOutput(fis1,[0 30],'Name',"tip1");
fis1 = addMF(fis1,"tip1","trimf",[0 5 10],'Name',"cheap");
fis1 = addMF(fis1,"tip1","trimf",[10 15 20],'Name',"average");
fis1 = addMF(fis1,"tip1","trimf",[20 25 30],'Name',"generous");
rulefis1a = "service==poor | food==rancid => tip1=cheap";
rulefis1b = "service==good => tip1=average";
rulefis1c = "service==excellent | food==delicious => tip1=generous";
rulefis1d = [rulefis1a rulefis1b rulefis1c];
fis1b = addRule(fis1,rulefis1d);
 
%Fuzzy model 2
fis2 = mamfis('Name',"tipper2");
fis2 = addInput(fis2,[0 10],'Name',"service");
fis2 = addMF(fis2,"service","gaussmf",[1.5 0],'Name',"poor");
fis2 = addMF(fis2,"service","gaussmf",[1.5 5],'Name',"good");
fis2 = addMF(fis2,"service","gaussmf",[1.5 10],'Name',"excellent");
fis2 = addInput(fis2,[0 10],'Name',"food");
fis2 = addMF(fis2,"food","trapmf",[-2 0 1 3],'Name',"rancid");
fis2 = addMF(fis2,"food","trapmf",[7 9 10 12],'Name',"delicious");
fis2 = addOutput(fis2,[0 30],'Name',"tip2");
fis2 = addMF(fis2,"tip2","trimf",[0 5 10],'Name',"cheap");
fis2 = addMF(fis2,"tip2","trimf",[10 15 20],'Name',"average");
fis2 = addMF(fis2,"tip2","trimf",[20 25 30],'Name',"generous");
rulefis2a = "service==poor | food==rancid => tip2=cheap";
rulefis2b = "service==good => tip2=average";
rulefis2c = "service==excellent | food==delicious => tip2=generous";
rulefis2d = [rulefis2a rulefis2b rulefis2c];
fis2b = addRule(fis2,rulefis2d);
%Fuzzy model 3
fis3 = mamfis('Name','fis3');
fis3 = addInput(fis3,[0 30],'Name',"tip1");
fis3 = addMF(fis3,"tip1","trimf",[0 5 10],'Name',"cheap");
fis3 = addMF(fis3,"tip1","trimf",[10 15 20],'Name',"average");
fis3 = addMF(fis3,"tip1","trimf",[20 25 30],'Name',"generous");
fis3 = addInput(fis3,[0 30],'Name',"tip2");
fis3 = addMF(fis3,"tip2","trimf",[0 5 10],'Name',"cheap");
fis3 = addMF(fis3,"tip2","trimf",[10 15 20],'Name',"average");
fis3 = addMF(fis3,"tip2","trimf",[20 25 30],'Name',"generous");
fis3.Outputs(1).Name = "final_tip";
fis3 = addMF(fis3,"final_tip","trapmf",[-2 0 1 3],'Name',"low");
fis3 = addMF(fis3,"final_tip","trapmf",[7 9 10 12],'Name',"high");
rulefis3a = "tip1==cheap | tip2==average => final_tip=low";
rulefis3b = "tip1==average => final_tip=low";
rulefis3c = "tip1==generous | tip2==generous => final_tip=high";
rulefis3d = [rulefis3a rulefis3b rulefis3c];
fis3b = addRule(fis3,rulefis3d);
con1 = ["tipper1/tip1" "fis3/tip1"];
con2 = ["tipper2/tip2" "fis3/tip2"];
aggTree = fistree([fis1b fis2b fis3],[con1;con2]);
aggTree.FIS(3) = addRule(aggTree.FIS(3), rulefis3d)
output = evalfis(aggTree,[0.2 0.25 0.3 0.2])

MATLAB: I am unable to create a fuzzy in matlab R2012b using code

That code works fine for me in R2012b.

Perhaps you do not have the Fuzzy Logic Toolbox installed, or do not have it licensed.

Best Answer

Related Solutions

MATLAB: Error with Fuzzy Tree Model

MATLAB: I am unable to create a fuzzy in matlab R2012b using code

Related Question