Дипломная работа: Синтез гибридной нейронной сети для управления двухмассовой электромеханической системой с зазором. 2003 г
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131
nn(2,j)=1;
end
end
end
case {3}
for i=1:m
for j=1:m
for j1=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1)]);
% Die Erhaltung des Indexes der minimalen Komponente des Vektores mf
% switch F(k)
% case mf(1,i)
% nn(1,i)=1;
% case mf(2,j)
% nn(2,j)=1;
% case mf(3,j1)
% nn(3,j1)=1;
% otherwise
% end
nn=[ 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ;
0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 ;
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 ;
1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 ;
0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 ;
0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1;
1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 ;
0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 ;
0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 ] ;
end
end
end
case 4
for i=1:m
for j=1:m
for j1=1:m
for j2=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2)]);
% Die Erhaltung des Indexes der minimalen Komponente des Vektores mf
switch F(k)
case mf(1,i)
nn(1,i)=1;
case mf(2,j)
nn(2,j)=1;
case mf(3,j1)
nn(3,j1)=1;
132
case mf(4,j2)
nn(4,j2)=1;
otherwise
end
end
end
end
end
case 5
for i=1:m
for j=1:m
for j1=1:m
for j2=1:m
for j3=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2);mf(5,j3)]);
end
end
end
end
end
case 6
for i=1:m
for j=1:m
for j1=1:m
for j2=1:m
for j3=1:m
for j4=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2);mf(5,j3);mf(6,j4)]);
end
end
end
end
end
end
case 7
for i=1:m
for j=1:m
for j1=1:m
for j2=1:m
for j3=1:m
for j4=1:m
for j5=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2);mf(5,j3);mf(6,j4);mf(7,j5)]);
end
end
133
end
end
end
end
end
case 8
for i=1:m
for j=1:m
for j1=1:m
for j2=1:m
for j3=1:m
for j4=1:m
for j5=1:m
for j6=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2);mf(5,j3);mf(6,j4);mf(7,j5);mf(8,j6)]);
end
end
end
end
end
end
end
end
case 9
for i=1:m
for j=1:m
for j1=1:m
for j2=1:m
for j3=1:m
for j4=1:m
for j5=1:m
for j6=1:m
for j7=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2);mf(5,j3);mf(6,j4);mf(7,j5);mf(8,j6);mf(9,j7)]);
end
end
end
end
end
end
end
end
end
case 10
for i=1:m
for j=1:m
for j1=1:m
134
for j2=1:m
for j3=1:m
for j4=1:m
for j5=1:m
for j6=1:m
for j7=1:m
for j8=1:m
k=k+1;
F(k)=min([mf(1,i);mf(2,j);mf(3,j1);mf(4,j2);mf(5,j3);mf(6,j4);mf(7,j5);mf(8,j6);mf(9,j7);mf(10,j
8)]);
end
end
end
end
end
end
end
end
end
end
otherwise
disp('Der Fehler der Dimension - der Ausgang fu:r die Grenzen der Bestimmung n>10 ! ');
disp('function rules.m')
end
135
Приложение В.
Начальные параметры нейронной сети ANFIS в пакете Matlab 6.1.
% общие параметры системы
[System]
Name='Untitled'
Type='sugeno'
Version=2.0
NumInputs=3
NumOutputs=1
NumRules=27
AndMethod='prod'
OrMethod='probor'
ImpMethod='min'
AggMethod='max'
DefuzzMethod='wtaver'
% характеристика первого входа
[Input1]
Name='input1'
Range=[0 2.87890625] % диапазон изменения сигнала
NumMFs=3 % колличество функций принадлежности
MF1='in1mf1':'gaussmf',[0.611279459777607 0] % ширина и центр функции Гаусса
MF2='in1mf2':'gaussmf',[0.611279459777607 1.439453125]
MF3='in1mf3':'gaussmf',[0.611279459777607 2.87890625]
[Input2]
Name='input2'
Range=[0 2.87890625]
NumMFs=3
MF1='in2mf1':'gaussmf',[0.611279459777607 0]
MF2='in2mf2':'gaussmf',[0.611279459777607 1.439453125]
MF3='in2mf3':'gaussmf',[0.611279459777607 2.87890625]
[Input3]
Name='input3'
Range=[0 2.87890625]
NumMFs=3
MF1='in3mf1':'gaussmf',[0.611279459777607 0]
MF2='in3mf2':'gaussmf',[0.611279459777607 1.439453125]
MF3='in3mf3':'gaussmf',[0.611279459777607 2.87890625]
%Параметры вывода. Коэффициенты линейной комбинации
[Output1]
Name='output'
Range=[0 4.1640625]
NumMFs=27
MF1='out1mf1':'constant',0
MF2='out1mf2':'constant',0
MF3='out1mf3':'constant',0
MF4='out1mf4':'constant',0
MF5='out1mf5':'constant',0
MF6='out1mf6':'constant',0
MF7='out1mf7':'constant',0
MF8='out1mf8':'constant',0
136
MF9='out1mf9':'constant',0
MF10='out1mf10':'constant',0
MF11='out1mf11':'constant',0
MF12='out1mf12':'constant',0
MF13='out1mf13':'constant',0
MF14='out1mf14':'constant',0
MF15='out1mf15':'constant',0
MF16='out1mf16':'constant',0
MF17='out1mf17':'constant',0
MF18='out1mf18':'constant',0
MF19='out1mf19':'constant',0
MF20='out1mf20':'constant',0
MF21='out1mf21':'constant',0
MF22='out1mf22':'constant',0
MF23='out1mf23':'constant',0
MF24='out1mf24':'constant',0
MF25='out1mf25':'constant',0
MF26='out1mf26':'constant',0
MF27='out1mf27':'constant',0
% комбинации слоя функций принадлежности с узлами слоя правил.
[Rules]
1 1 1, 1 (1) : 1
1 1 2, 2 (1) : 1
1 1 3, 3 (1) : 1
1 2 1, 4 (1) : 1
1 2 2, 5 (1) : 1
1 2 3, 6 (1) : 1
1 3 1, 7 (1) : 1
1 3 2, 8 (1) : 1
1 3 3, 9 (1) : 1
2 1 1, 10 (1) : 1
2 1 2, 11 (1) : 1
2 1 3, 12 (1) : 1
2 2 1, 13 (1) : 1
2 2 2, 14 (1) : 1
2 2 3, 15 (1) : 1
2 3 1, 16 (1) : 1
2 3 2, 17 (1) : 1
2 3 3, 18 (1) : 1
3 1 1, 19 (1) : 1
3 1 2, 20 (1) : 1
3 1 3, 21 (1) : 1
3 2 1, 22 (1) : 1
3 2 2, 23 (1) : 1
3 2 3, 24 (1) : 1
3 3 1, 25 (1) : 1
3 3 2, 26 (1) : 1
3 3 3, 27 (1) : 1
[System]
Name='Untitled_learn'
137
Type='sugeno'
Version=2.0
NumInputs=3
NumOutputs=1
NumRules=27
AndMethod='prod'
OrMethod='probor'
ImpMethod='min'
AggMethod='max'
DefuzzMethod='wtaver'
[Input1]
Name='input1'
Range=[0 2.87890625]
NumMFs=3
MF1='in1mf1':'gaussmf',[0.611503890391397 2.45420121989906e-005]
MF2='in1mf2':'gaussmf',[0.61152462413984 1.43963470340157]
MF3='in1mf3':'gaussmf',[0.611203797331831 2.87895140448571]
[Input2]
Name='input2'
Range=[0 2.87890625]
NumMFs=3
MF1='in2mf1':'gaussmf',[0.61001223406247 -0.00162577150661728]
MF2='in2mf2':'gaussmf',[0.605146829591237 1.44110620323938]
MF3='in2mf3':'gaussmf',[0.611120425388344 2.87900268133769]
[Input3]
Name='input3'
Range=[0 2.87890625]
NumMFs=3
MF1='in3mf1':'gaussmf',[0.613318490222482 0.0018126867439601]
MF2='in3mf2':'gaussmf',[0.617969425611638 1.43771530879766]
MF3='in3mf3':'gaussmf',[0.611300707866361 2.87896286343304]
[Output1]
Name='output'
Range=[0 4.1640625]
NumMFs=27
MF1='out1mf1':'constant',19.9068214202377
MF2='out1mf2':'constant',-983.480559547255
MF3='out1mf3':'constant',-2.40821447824513
MF4='out1mf4':'constant',808.155613273162
MF5='out1mf5':'constant',-9.19486280702087
MF6='out1mf6':'constant',-364.677173168821
MF7='out1mf7':'constant',3.69332901168902
MF8='out1mf8':'constant',-9.24360038682111
MF9='out1mf9':'constant',152.060660125491
MF10='out1mf10':'constant',33.381071349373
MF11='out1mf11':'constant',-597.059318585449
MF12='out1mf12':'constant',-395.65093412213
MF13='out1mf13':'constant',454.935432737635
138
MF14='out1mf14':'constant',2.9590330565026
MF15='out1mf15':'constant',9.45530960915196
MF16='out1mf16':'constant',589.810053032066
MF17='out1mf17':'constant',0.641932798988635
MF18='out1mf18':'constant',-2.23266215252474
MF19='out1mf19':'constant',-2.77655442470999
MF20='out1mf20':'constant',-270.624506942634
MF21='out1mf21':'constant',-63.821095358477
MF22='out1mf22':'constant',275.912333610695
MF23='out1mf23':'constant',1.37606729568691
MF24='out1mf24':'constant',-3.34716415756246
MF25='out1mf25':'constant',140.379889261655
MF26='out1mf26':'constant',16.0781005014565
MF27='out1mf27':'constant',3.93034165041184
[Rules]
1 1 1, 1 (1) : 1
1 1 2, 2 (1) : 1
1 1 3, 3 (1) : 1
1 2 1, 4 (1) : 1
1 2 2, 5 (1) : 1
1 2 3, 6 (1) : 1
1 3 1, 7 (1) : 1
1 3 2, 8 (1) : 1
1 3 3, 9 (1) : 1
2 1 1, 10 (1) : 1
2 1 2, 11 (1) : 1
2 1 3, 12 (1) : 1
2 2 1, 13 (1) : 1
2 2 2, 14 (1) : 1
2 2 3, 15 (1) : 1
2 3 1, 16 (1) : 1
2 3 2, 17 (1) : 1
2 3 3, 18 (1) : 1
3 1 1, 19 (1) : 1
3 1 2, 20 (1) : 1
3 1 3, 21 (1) : 1
3 2 1, 22 (1) : 1
3 2 2, 23 (1) : 1
3 2 3, 24 (1) : 1
3 3 1, 25 (1) : 1
3 3 2, 26 (1) : 1
3 3 3, 27 (1) : 1