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])
