Metode razvoja i adaptacije regresionih modela bazirane na genetskim algoritmima
Докторанд
Milivojević, MilovanМентор
Stojanović, BobanЧланови комисије
Divac, DejanRanković, Vladimir
Ivanović, Miloš
Метаподаци
Приказ свих података о дисертацијиСажетак
Većina postojećih regresionih metoda modeliranja pretpostavlja vremensku
nepromenljivost modeliranih objekata i zahteva stalan skup ulaznih parametara. U
realnim aplikacijama, stalne promene objekata i otkazi merne opreme mogu dovesti do
situacija u kojima usvojeni model postaje neupotrebljiv. Iz tog razloga je neophodno
razviti metode i sisteme za automatsko generisanje što adekvatnijih modela za datu
situaciju. U okviru ove disertacije su razvijena dva hibridna metoda koji nude deo
rešenja za navedene probleme.
MLR/GA hibrid omogućava generisanje linearnog regresionog modela (MLR)
koji je, za date uslove, optimizovan pomoću genetskih algoritama po kriterijumu
tačnosti i kriterijumu kompleksnosti. Za razliku od postojećih metoda, MLR/GA
metod omogućava generisanje adaptivnih modela koji su otporni na promenljivost
skupa ulaznih promenljivih i promenljivost skupa izmerenih vrednosti. Razvijeni
MLR/GA metod je implementiran u vidu GenReg softverskog agenta, čije performanse
su testirane... u postupku modeliranja radijalnog pomeranja odabranih tačaka betonske
brane Bočac, na reci Vrbas u Republici Srpskoj. Modeli generisani korišćenjem
MLR/GA metoda su u slučaju otkaza pojedinih senzora pokazali značajno bolju
sposobnost za predikciju u odnosu na MLR modele koji podrazumevaju stalan skup
ulaznih promenljivih. Dodatno, hibridni metod je pokazao sposobnost da pri
generisanju modela odbacuje prediktore koji nisu od značaja za opisivanje posmatranog
objekta.
ANN/GA je hibridni metod za razvoj i adaptaciju regresionih modela
zasnovanih na veštačkim neuronskim mrežama (ANN). Korišćenjem genetskih
algoritama ANN/GA metod optimizuje strukturu i parametre neuronske mreže u
skladu sa aktuelnim skupovima ulaznih i izlaznih promenljivih, i merenih
vrednosti. Za razliku od sličnih postojećih rešenja, ANN/GA metod optimizuje skoro
sve elemente neuronske mreže. Hibrid vrši samopodešavanje modela tako što
optimizuje broj skrivenih slojeva, broj neurona u tim slojevima, izbor aktivacione
funkcije, algoritam učenja, kao i vrednosti parametara učenja u skladu sa odabranim
algoritmom. Razvijeni ANN/GA metod je implementiran u vidu DEVONNA
softverskog agenta koji je validovan kroz studiju slučaja brane Grančarevo, na reci
Tebišnjici u Republici Srpskoj, a rezultati su poređeni sa rezultatima dobijenim
korišćenjem ekvivalentnog MLR/GA hibrida. Realizovani testovi su pokazali da
modeli generisani ANN/GA hibridom mogu dati predikcije strukturnog ponašanja
brane sa većom tačnošću od MLR modela. Međutim, za razliku od modela u obliku
MLR, koji su otporni na temperaturne fazne pomake prisutne na različitim
geografskim lokacijama, modeli u formi ANN pokazuju nestabilno ponašanje pod
takvim okolnostima. Pored toga, generisanje ANN modela je vremenski znatno
zahtevnije.
Komparativna analiza modela generisanih na osnovu MLR/GA i ANN/GA metoda
sa jedne, i modela u formi postepenih regresija, sa druge strane, je pokazala da
predstavljeni metodi u pojedinim aspektima prevazilaze mogućnosti postojećih
metoda za generisanje regresionih modela. Uz primenu tehnika redukcije dimenzija
prostora istraživanja, predloženi hibridni metodi i razvijeni softverski agenti
predstavljaju moćan alat za modeliranje realnih objekata i sistema.
Most of existing regression modeling methods presuppose the time immutability of the modeled objects and require a constant set of input parameters. In real applications, the constant changes of the objects and failures of measuring equipment can lead to situations in which the adopted model becomes unusable. For this reason it is necessary to develop
methods and systems for automatic generation of the most adequate models for the given
situation. In this dissertation two hybrid methods that offer part of the solution to the above
problems have been developed.
MLR/GA hybrid is able to generate a linear regression model (MLR) which is, for the
given conditions, optimized by using genetic algorithms according to the criterion of accuracy
and complexity criterion. Unlike the existing methods, MLR/GA method is enable to generate
the adaptive models that are resistant to the variability of the set of input variables and the
growing set of measured values. The developed MLR/GA method is impl...emented in the form
of GenReg software agent, whose performances have been tested in the process of modeling
the radial displacement of the selected points of Bočac concrete dam on the Vrbas river, in the
Republic of Srpska. In the case of failure of individual sensors, models generated by using
MLR/GA method showed a significantly better prediction compared to the MLR models that
implied a constant set of input variables. In addition, the hybrid method has shown the
capability of rejecting predictors that have no influence on the modeled object.
ANN/GA is a hybrid method for the development and adaptation of regression models
based on artificial neural networks (ANN). Using genetic algorithms ANN/GA method
optimizes the structure and parameters of neural network in accordance with the current sets
of input and output variables and measured values. Unlike similar existing solutions, ANN/GA
method optimizes nearly all the elements of a neural network. The hybrid performs self-tuning
of the model by optimizing the number of hidden layers, the number of neurons in these
layers, the choice of activation function, learning algorithm, as well as the values of learning
parameters of the selected algorithm. The developed ANN/GA method was implemented in the
form of DEVONNA software agent that was validated through a case study Grancarevo, on
the Tebisnjica river, in the Republic of Srpska, and the results were compared to the results
obtained using the equivalent MLR/GA hybrid. Completed tests showed that the models
generated by ANN/GA hybrid could give predictions of structural behavior of the dam with a
higher accuracy than the MLR model. However, unlike the models in the form of MLR, which
are resistant to temperature phase offsets present at different geographical locations, the
models in the form of ANN exhibit unstable behavior under such circumstances. In addition,
the generation of an ANN model has shown much higher computational demands.
The comparative analysis of the models generated by the MLR/GA and ANN/GA
methods on the one hand, and the models in the form of stepwise regression, on the other
hand, has shown that the presented methods in some aspects surpass the capabilities of
existing methods for generating the regression models. With the application of the research
space dimension reduction the proposed hybrid methods and the developed software agents
represent a powerful tool for modeling real objects and systems.