(meta)modelarea circuitelor electronice€¦ · (meta)modelarea circuitelor electronice simularea...
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(Meta)modelarea circuitelor electronice➢ Simularea la nivel de circuit este costisitoare din punct de vedere al
calculelor, în special dacă circuitul este complex şi necesită mai
multe tipuri de analize (în timp, în frecvenţă, etc.).
➢ Alternativa: nivele ierarhic superioare de abstractizare în descrierea
circuitului; utilizare modele compacte.
➢ Doi factori determină utilitatea modelului:
❖ eficient din punct de vedere al volumului şi complexităţii calculelor
❖ precizia (suficient de exact).
Modelarea
circuitelor
• modelarea
functiilor de
performanta
(statice)
• modelarea
functionala
(dinamice)
(Meta)modelarea circuitelor electroniceutilizand sisteme fuzzy
➢ Modelarea circuitelor electronice
❖ modelarea functiilor de performanta
▪ modelare SOTA
❖ modelarea functionala
▪ modelare FCOTA
▪ model Simulink
Modelarea functiilor de performanta ale unui circuit
analogic
SOTA – Simple Operational Transconductance amplifier
( )
( )
( ) ( ) 1cu;1;100
0,75μcu;1;10
0,5μcu;1;8
μA20;70
5656
3434
1212
=
=
=
LWW,L
LLW
LLW
IB
•Parametrii
• Functii de performanta:
21 ii
o
vv
vAvo
−=
BAvoGBW =
PM
cA
AvoCMRR =
Procedura de
modelareDesign of
experiment
! Metamodel (surrogate model);
a model of the model
❖ Generally speaking, a metamodel, or a surrogate model, is a
model of the model, i.e. a simplified model of an actual model
of a circuit, system, or software like entity.
❖ A metamodel can be a mathematical relation or algorithm
representing input and output relations.
❖ A model is an abstraction of phenomena in the real world; a
metamodel is yet another abstraction, highlighting properties
of the model itself.
❖Metamodeling typically involves studying the output and input
relationships and then fitting proper metamodels to represent
that behavior.
Metamodeling
➢ Once the metamodels are generated, the designer can conduct
more extensive analyses of the circuit and use the same
metamodel for different criteria to be optimized.
➢ The detailed simulation is significantly more time
consuming than using the metamodel.
➢ The key points of metamodeling:
▪ accuracy - capability of generating the system response over the
design space;
▪ efficiency - computational effort required for constructing the
metamodel;
▪ transparency - capability of providing the information concerning
contributions and variations of design variables and correlation
among the variables;
▪ simplicity - simple methods should require less user input and be
easily adapted to different problem.
Metamodeling
Determinarea setului de parametric (DoE)
➢ LHS – Latin Hypercube Sample (+ Full Factorial Design)
• Domeniul fiecarei variabile se imparte in K intervale (egale)
• În fiecare interval se alege aleator o valoare.
• Cele K valori ale fiecărui parametru sunt asociate în mod aleator cu cele
K valori ale altui parametru ş.a.m.d. rezultand K vectori ai parametrilor.
• LHS
• 2 level Full Factorial Design – toate combinatiile posibile ale
parametrilor, considerand numai valorile extreme (minim si maxim)
DoE
9 /29
❑ For the design of the experiment, it is necessary to generate those
parameter combinations that fill in the parameter space, in order to
encompass all the regions (in the parameter space).
❑ A good experimental design (ED) is essential to simultaneously reduce
the possible effect of noise and bias error.
❑ It is recommended to construct an experimental design by combining
multiple techniques for design experiment to reduce the risk of using a
poor ED.
LHS designs may leave out the boundary and the final model may lead to large
extrapolation errors. To avoid this, one should generate the experimental design by
mixing the LHS design with a 2 level (or n level) full factorial design:
1. Latin Hypercube sampling is used to generate a specified number of randomly
distributed values for each parameter, these values being randomly permuted to
obtain different parameter combinations.
2. Two-level full-factorial design is used to generate all possible parameter
combinations, considering only the extreme values (minimum and maximum) for all
parameter
Esantion al setului de date de antrenare
Structura modelului fuzzy Avo
• Sistem fuzzy
TS de ordin 1
Multimi fuzzy la intrare
• 6 reguli
• 6 mf pe fiecare
variabila
Multimi fuzzy la iesire. Reguli
•out1mf1=[-0.27907622925482 4.7336423208163 -0.22929012109304 -0.0028363221851113 45.49729161833]
•out1mf2=[-0.11955049624726 10.308484211334 -0.23201470784719 0.00408876941051388 25.0003432748168]
•out1mf3=[-0.50949884619065 5.4106958973798 -0.70345988469712 0.0282962177749871 52.5057718830839]
•out1mf4=[-0.32603181267357 11.099093462894 -0.98934580658441 0.0138103461621346 37.5543179917034]
•out1mf5=[-0.18956099089732 8.6928124102796 -0.96510752831766 0.016395396082041 33.7788736156701]
•out1mf6=[-0.38682872084570 7.5633681369785 -1.3443012661576 -0.042937570643951 49.8212643120914]
• Multimile fuzzy la iesire
• Baza de reguli
Evolutia RMSE pe durata instruirii
Suprapotrivire
(overfitting)
Suprapotrivire
(overfitting)
RMSE pentru modelele fuzzyFuncţia
de
circuit
Setul
de date
RMSE
3 reguli 6 reguli 10 reguli
antrenare verificare antrenare verif. antrenare verif.
Avo
450a+50v1.85
1.5
1.15
0.55
1.6
1.45
0.7
0.55
700a+150v1.39
1.27
1.16
1.05
1.29
1.18
1.06
0.93
1.21
1.15
1.00
0.93
GBW
[KHz]
450a+50v178
132
176
115
130
88
124
84
106
83
79
64
700a+150v205
156
142
89
155
142
80
58
145
140
68
65
PM [o]
450a+50v0.142
0.115
0141
0.115
0.115
0.110
0.079
0.056
0.116
0.108
0.084
0.073
700a+150v0.116
0.096
0.063
0.040
0.102
0.090
0.054
0.033
0.98
0.090
0.050
0.036
CMRR
450a+50v101735
95640
61735
46408
72200
70100
61735
46408
700a+150v107279
80529
72116
35201
36800
33400
27823
43875
75311
75188
30750
30706
• RMSE este dependenta de ordinul de marime
al functiei modelate( )
=
−
K
k
kdk yy
K1
21
Comparație model fuzzy –simulare Spice
Funcţia de
circuit
EPM [%]
instruire verificare
Avo 1,375 1,278
GBW 2,645 1,921
PM 0,049 0,0398
CMRR 3,04 4,67
•EPM – eroarea procentuala
medie
Suprafetele generate de modelele fuzzy
Modelarea functională a unui circuit analogic
Procedura de metamodelare
FCOTA – Folded Cascode OTA
Modelul functional fuzzy
Colectarea datelor
necesare modelarii
Structura modelului fuzzy
Antrenare
Evoluţia erorilor la antrenarea sistemului fuzzy
amplificare(frecvenţă, temperatură) pentru circuitul FCOTA.
Reguli
Suprafetele generate de modelele fuzzy
• Sistemul fuzzy
dupa antrenare
• Sistemul fuzzy
initial
Comparatie model fuzzy –simulare Spice
Modelul functional fuzzy
Implementare Simulink
Rezultate -1
Rezultate - 2
Exercitiu
32 /29
Graficele cu evolutia erorilor pentru
instruirea cu ANFIS a unor sisteme fuzzy
sunt prezentate in figurile alaturate.
Caracterizati procesul de instruire si realizati
o recomnadare referitoare la obtinerea unui
sistem fuzzy final optim, pastrand aceeasi
configuratie a slf si a setului de date.