32 resultados para Non linear control
em Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland
Resumo:
A rotating machine usually consists of a rotor and bearings that supports it. The nonidealities in these components may excite vibration of the rotating system. The uncontrolled vibrations may lead to excessive wearing of the components of the rotating machine or reduce the process quality. Vibrations may be harmful even when amplitudes are seemingly low, as is usually the case in superharmonic vibration that takes place below the first critical speed of the rotating machine. Superharmonic vibration is excited when the rotational velocity of the machine is a fraction of the natural frequency of the system. In such a situation, a part of the machine’s rotational energy is transformed into vibration energy. The amount of vibration energy should be minimised in the design of rotating machines. The superharmonic vibration phenomena can be studied by analysing the coupled rotor-bearing system employing a multibody simulation approach. This research is focused on the modelling of hydrodynamic journal bearings and rotorbearing systems supported by journal bearings. In particular, the non-idealities affecting the rotor-bearing system and their effect on the superharmonic vibration of the rotating system are analysed. A comparison of computationally efficient journal bearing models is carried out in order to validate one model for further development. The selected bearing model is improved in order to take the waviness of the shaft journal into account. The improved model is implemented and analyzed in a multibody simulation code. A rotor-bearing system that consists of a flexible tube roll, two journal bearings and a supporting structure is analysed employing the multibody simulation technique. The modelled non-idealities are the shell thickness variation in the tube roll and the waviness of the shaft journal in the bearing assembly. Both modelled non-idealities may cause subharmonic resonance in the system. In multibody simulation, the coupled effect of the non-idealities can be captured in the analysis. Additionally one non-ideality is presented that does not excite the vibrations itself but affects the response of the rotorbearing system, namely the waviness of the bearing bushing which is the non-rotating part of the bearing system. The modelled system is verified with measurements performed on a test rig. In the measurements the waviness of bearing bushing was not measured and therefore it’s affect on the response was not verified. In conclusion, the selected modelling approach is an appropriate method when analysing the response of the rotor-bearing system. When comparing the simulated results to the measured ones, the overall agreement between the results is concluded to be good.
Resumo:
The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.
Resumo:
Teollisuuden tuotannon eri prosessien optimointi on hyvin ajankohtainen aihe. Monet ohjausjärjestelmät ovat ajalta, jolloin tietokoneiden laskentateho oli hyvin vaatimaton nykyisiin verrattuna. Työssä esitetään tuotantoprosessi, joka sisältää teräksen leikkaussuunnitelman muodostamisongelman. Valuprosessi on yksi teräksen valmistuksen välivaiheita. Siinä sopivaan laatuun saatettu sula teräs valetaan linjastoon, jossa se jähmettyy ja leikataan aihioiksi. Myöhemmissä vaiheissa teräsaihioista muokataan pienempiä kokonaisuuksia, tehtaan lopputuotteita. Jatkuvavaletut aihiot voidaan leikata tilauskannasta riippuen monella eri tavalla. Tätä varten tarvitaan leikkaussuunnitelma, jonka muodostamiseksi on ratkaistava sekalukuoptimointiongelma. Sekalukuoptimointiongelmat ovat optimoinnin haastavin muoto. Niitä on tutkittu yksinkertaisempiin optimointiongelmiin nähden vähän. Nykyisten tietokoneiden laskentateho on kuitenkin mahdollistanut raskaampien ja monimutkaisempien optimointialgoritmien käytön ja kehittämisen. Työssä on käytetty ja esitetty eräs stokastisen optimoinnin menetelmä, differentiaalievoluutioalgoritmi. Tässä työssä esitetään teräksen leikkausoptimointialgoritmi. Kehitetty optimointimenetelmä toimii dynaamisesti tehdasympäristössä käyttäjien määrittelemien parametrien mukaisesti. Työ on osa Syncron Tech Oy:n Ovako Bar Oy Ab:lle toimittamaa ohjausjärjestelmää.
Resumo:
This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.
Resumo:
Tuotantotehokkuus näyttelee yhä suurempaa roolia teollisuudessa, minkä vuoksi myös pakkauslinjastoille joudutaan asettamaan suuria vaatimuksia. Usein leikkaus- ja kappaleensiirtosovelluksissa käytetään lineaarisia ruuvikäyttöjä, jotka voitaisiin tietyin edellytyksin korvata halvemmilla ja osittain suorituskykyisimmillä hammashihnavetoisilla johteilla. Yleensä paikkasäädetty työsolu muodostuu kahden tai kolmen eri koordinaatistoakselin suuntaan asennetuista johteista. Tällaisen työsolun paikoitustarkkuuteen vaikuttavat muun muassa käytetty säätörakenne, moottorisäätöketjun viiveet, sekä laitteiston eri epälineaarisuudet, kuten kitka. Tässä työssä esitetään lineaarisen hammashihnaservokäytön dynaamista käytöstä kuvaava matemaattinen malli ja laaditaan mallin pohjalta laitteen simulointimalli. Mallin toimivuus varmistetaan käytännön identifiointitesteillä. Lisäksi työssä tutkitaan, kuinka hyvään suorituskykyyn lineaarinen hammashihnaservokäyttö kykenee, jos teollisuudessa paikoitussäätörakenteena tyypillisesti käytetty kaskadirakenne tai PID-rakenne korvataan kehittyneemmällä mallipohjaisella tilasäädinrakenteella. Säädön toimintaa arvioidaan simulointien ja koelaitteistolla suoritettavien mittausten perusteella.
Resumo:
Rosin is a natural product from pine forests and it is used as a raw material in resinate syntheses. Resinates are polyvalent metal salts of rosin acids and especially Ca- and Ca/Mg- resinates find wide application in the printing ink industry. In this thesis, analytical methods were applied to increase general knowledge of resinate chemistry and the reaction kinetics was studied in order to model the non linear solution viscosity increase during resinate syntheses by the fusion method. Solution viscosity in toluene is an important quality factor for resinates to be used in printing inks. The concept of critical resinate concentration, c crit, was introduced to define an abrupt change in viscosity dependence on resinate concentration in the solution. The concept was then used to explain the non-inear solution viscosity increase during resinate syntheses. A semi empirical model with two estimated parameters was derived for the viscosity increase on the basis of apparent reaction kinetics. The model was used to control the viscosity and to predict the total reaction time of the resinate process. The kinetic data from the complex reaction media was obtained by acid value titration and by FTIR spectroscopic analyses using a conventional calibration method to measure the resinate concentration and the concentration of free rosin acids. A multivariate calibration method was successfully applied to make partial least square (PLS) models for monitoring acid value and solution viscosity in both mid-infrared (MIR) and near infrared (NIR) regions during the syntheses. The calibration models can be used for on line resinate process monitoring. In kinetic studies, two main reaction steps were observed during the syntheses. First a fast irreversible resination reaction occurs at 235 °C and then a slow thermal decarboxylation of rosin acids starts to take place at 265 °C. Rosin oil is formed during the decarboxylation reaction step causing significant mass loss as the rosin oil evaporates from the system while the viscosity increases to the target level. The mass balance of the syntheses was determined based on the resinate concentration increase during the decarboxylation reaction step. A mechanistic study of the decarboxylation reaction was based on the observation that resinate molecules are partly solvated by rosin acids during the syntheses. Different decarboxylation mechanisms were proposed for the free and solvating rosin acids. The deduced kinetic model supported the analytical data of the syntheses in a wide resinate concentration region, over a wide range of viscosity values and at different reaction temperatures. In addition, the application of the kinetic model to the modified resinate syntheses gave a good fit. A novel synthesis method with the addition of decarboxylated rosin (i.e. rosin oil) to the reaction mixture was introduced. The conversion of rosin acid to resinate was increased to the level necessary to obtain the target viscosity for the product at 235 °C. Due to a lower reaction temperature than in traditional fusion synthesis at 265 °C, thermal decarboxylation is avoided. As a consequence, the mass yield of the resinate syntheses can be increased from ca. 70% to almost 100% by recycling the added rosin oil.
Resumo:
This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.
Resumo:
One challenge on data assimilation (DA) methods is how the error covariance for the model state is computed. Ensemble methods have been proposed for producing error covariance estimates, as error is propagated in time using the non-linear model. Variational methods, on the other hand, use the concepts of control theory, whereby the state estimate is optimized from both the background and the measurements. Numerical optimization schemes are applied which solve the problem of memory storage and huge matrix inversion needed by classical Kalman filter methods. Variational Ensemble Kalman filter (VEnKF), as a method inspired the Variational Kalman Filter (VKF), enjoys the benefits from both ensemble methods and variational methods. It avoids filter inbreeding problems which emerge when the ensemble spread underestimates the true error covariance. In VEnKF this is tackled by resampling the ensemble every time measurements are available. One advantage of VEnKF over VKF is that it needs neither tangent linear code nor adjoint code. In this thesis, VEnKF has been applied to a two-dimensional shallow water model simulating a dam-break experiment. The model is a public code with water height measurements recorded in seven stations along the 21:2 m long 1:4 m wide flume’s mid-line. Because the data were too sparse to assimilate the 30 171 model state vector, we chose to interpolate the data both in time and in space. The results of the assimilation were compared with that of a pure simulation. We have found that the results revealed by the VEnKF were more realistic, without numerical artifacts present in the pure simulation. Creating a wrapper code for a model and DA scheme might be challenging, especially when the two were designed independently or are poorly documented. In this thesis we have presented a non-intrusive approach of coupling the model and a DA scheme. An external program is used to send and receive information between the model and DA procedure using files. The advantage of this method is that the model code changes needed are minimal, only a few lines which facilitate input and output. Apart from being simple to coupling, the approach can be employed even if the two were written in different programming languages, because the communication is not through code. The non-intrusive approach is made to accommodate parallel computing by just telling the control program to wait until all the processes have ended before the DA procedure is invoked. It is worth mentioning the overhead increase caused by the approach, as at every assimilation cycle both the model and the DA procedure have to be initialized. Nonetheless, the method can be an ideal approach for a benchmark platform in testing DA methods. The non-intrusive VEnKF has been applied to a multi-purpose hydrodynamic model COHERENS to assimilate Total Suspended Matter (TSM) in lake Säkylän Pyhäjärvi. The lake has an area of 154 km2 with an average depth of 5:4 m. Turbidity and chlorophyll-a concentrations from MERIS satellite images for 7 days between May 16 and July 6 2009 were available. The effect of the organic matter has been computationally eliminated to obtain TSM data. Because of computational demands from both COHERENS and VEnKF, we have chosen to use 1 km grid resolution. The results of the VEnKF have been compared with the measurements recorded at an automatic station located at the North-Western part of the lake. However, due to TSM data sparsity in both time and space, it could not be well matched. The use of multiple automatic stations with real time data is important to elude the time sparsity problem. With DA, this will help in better understanding the environmental hazard variables for instance. We have found that using a very high ensemble size does not necessarily improve the results, because there is a limit whereby additional ensemble members add very little to the performance. Successful implementation of the non-intrusive VEnKF and the ensemble size limit for performance leads to an emerging area of Reduced Order Modeling (ROM). To save computational resources, running full-blown model in ROM is avoided. When the ROM is applied with the non-intrusive DA approach, it might result in a cheaper algorithm that will relax computation challenges existing in the field of modelling and DA.
Resumo:
Työn tarkoituksena oli tutkia korkeakappa-massan suotautuvuutta sekä etsiä uusia analyysimenetelmiä korkeakappa-massan karakterisoimiseksi. Työssä pyrittiin määrittämään tekijöitä, jotka vaikuttavat korkeakappa-massan suotautumiseen. Työn kirjallisessa osassa tarkasteltiin aluksi yleisesti keiton teoriaa, minkä jälkeen käsiteltiin jauhatusta ja tarkemmin korkeakappa-massan hienovaraisempaa jauhatusta eli kuidutusta. Seuraavaksi käsiteltiin suodatusta ja sen teoriaa sekä suodatukseen vaikuttavia tekijöitä. Massan pesusta esitettiin perusteet ja teoriaa. Lopuksi tarkasteltiin massan karakterisointia eri lähestymistavoilla sekä kuitujen perusominaisuuksia. Kokeellisessa osassa verrattiin LTY:n koesuodatuslaitteistolla tehdyillä suodatuskokeilla korkeakappa-massaisen sellukakun suotautuvuutta eri paine-eroilla, suodoksen eri hienoainepitoisuuksilla sekä ennen ja jälkeen sellutehtaallatapahtuneen kuidutuksen. Savonlinnassa sijaitsevalla laitteistolla tehtiin syrjäytystestejä ennen ja jälkeen kuidutusta otetuilla sellumassoilla. Lisäksi ennenja jälkeen kuidutusta otettuja sellumassanäytteitä karakterisoitiin mm. kuituanalysaattorilla, huokoskoko- ja ominaispinta-ala-analyyseillä sekä SEM-kuvilla. Suodatuskokeissa hienoainepitoisuudella ei ollut merkitystä permeabiliteettiin mitattujen suodosvirtausten perusteella. Kuten Darcyn lain perusteella voitiin olettaa, kakun paine-eron kasvaessa permeabiliteetti kasvoi. Vaikutus ei ollut kuitenkaan lineaarinen paine-eroon verrattuna vaan kakun permeabiliteetti kasvoi enemmän tietyllä paine-erovälillä. Tämä paine-eroväli vaihteli hieman riippuen oliko sellumassa otettu ennen vai jälkeen kuidutusta. Lappeenrannassa tehdyissä suodatuskokeissa ei ennen ja jälkeen kuidutusta otetuilla näytteillä ollut selvää eroa permeabiliteeteissa, mutta Savonlinnan syrjäytystesteissä ero syrjäytymisnopeudessa oli selvä. Ennen ja jälkeen kuidutusta otettujen sellumassojen kuituanalysaattorituloksissa ja SEM-kuvissa ei havaittu eroa näytteiden välillä, mutta massojen huokoskoko muuttui kuidutuksen vaikutuksesta.
Resumo:
Tutkimuksessa arvioidaan millaisia kyvykkyyksiä toimijoiltavaaditaan, jotta voidaan edistää verkostoja palvelevan innovaatiopolitiikan toteutumista ja toteuttaa käytäntölähtöistä innovaatiotoimintaa. Empiirinen osa tarkastelee Päijät-Hämeen toimijoiden asenneympäristöä ja toimimisen valmiuksia käytäntölähtöisen innovaatiotoiminnan tarpeisiin sopivaksi. Osaaminen kerääntyy yliopistopaikkakunnille ulkoisten suurtuotannon etujen mukaisesti. Ne alueet, joilla ei ole yliopistoa joutuvat luomaan muunlaista innovaatiokyvykkyyttä saavuttaakseen kilpailuetua. Siksi Päijät-Hämeen visiona on tulla johtavaksi käytäntölähtöisen innovaatiotoiminnan alueeksi hyvien toimintamallien ja tehokkaiden tiedonsiirtomekanismien avulla. Tämä vaatii alueen toimijoilta mm. korkeaa absorptiivista kapasiteettia ja heikkoja linkkejä alueen ulkopuolelle. Työn empiirinen osa koostuu 12:sta puolistrukturoidusta haastattelusta sekä kyselytutkimuksesta. Tiedonluonti ja -siirto alueelle nähtiin pääasiassa tutkimusmaailman tehtävänä, mutta varianssianalyysin perusteella tutkimusmaailma ei itse nähnyt olevansa siinä asemassa. Yhteisen kielen puuttuminen tutkimus- ja käytännön työelämän väliltä nähtiin puutteena.
Resumo:
In this thesis, the sorption and elastic properties of the cation-exchange resins were studied to explain the liquid chromatographic separation of carbohydrates. Na+, Ca2+ and La3+ form strong poly(styrene-co-divinylbenzene) (SCE) as well as Na+ and Ca2+ form weak acrylic (WCE) cation-exchange resins at different cross-link densities were treated within this work. The focus was on the effects of water-alcohol mixtures, mostly aqueous ethanol, and that of the carbohydrates. The carbohydrates examined were rhamnose, xylose, glucose, fructose, arabinose, sucrose, xylitol and sorbitol. In addition to linear chromatographic conditions, non-linear conditions more typical for industrial applications were studied. Both experimental and modeling aspectswere covered. The aqueous alcohol sorption on the cation-exchangers were experimentally determined and theoretically calculated. The sorption model includes elastic parameters, which were obtained from sorption data combined with elasticity measurements. As hydrophilic materials cation-exchangers are water selective and shrink when an organic solvent is added. At a certain deswelling degree the elastic resins go through glass transition and become as glass-like material. Theincreasing cross-link level and the valence of the counterion decrease the sorption of solvent components in the water-rich solutions. The cross-linkage or thecounterions have less effect on the water selectivity than the resin type or the used alcohol. The amount of water sorbed is higher in the WCE resin and, moreover, the WCE resin is more water selective than the corresponding SCE resin. Theincreased aliphatic part of lower alcohols tend to increase the water selectivity, i.e. the resins are more water selective in 2-propanol than in ethanol solutions. Both the sorption behavior of carbohydrates and the sorption differences between carbohydrates are considerably affected by the eluent composition and theresin characteristics. The carbohydrate sorption was experimentally examined and modeled. In all cases, sorption and moreover the separation of carbohydrates are dominated by three phenomena: partition, ligand exchange and size exclusion. The sorption of hydrophilic carbohydrates increases when alcohol is added into the eluent or when carbohydrate is able to form coordination complexes with the counterions, especially with multivalent counterions. Decreasing polarity of the eluent enhances the complex stability. Size exclusion effect is more prominent when the resin becomes tighter or carbohydrate size increases. On the other hand,the elution volumes between different sized carbohydrates decreases with the decreasing polarity of the eluent. The chromatographic separation of carbohydrateswas modeled, using rhamnose and xylose as target molecules. The thermodynamic sorption model was successfully implemented in the rate-based column model. The experimental chromatographic data were fitted by using only one adjustable parameter. In addition to the fitted data also simulated data were generated and utilized in explaining the effect of the eluent composition and of the resin characteristics on the carbohydrate separation.
Resumo:
Tässä työssä tutkitaan ohjelmistoarkkitehtuurisuunnitteluominaisuuksien vaikutusta erään client-server –arkkitehtuuriin perustuvan mobiilipalvelusovelluksen suunnittelu- ja toteutusaikaan. Kyseinen tutkimus perustuu reaalielämän projektiin, jonka kvalitatiivinen analyysi paljasti arkkitehtuurikompponenttien välisten kytkentöjen merkittävästi vaikuttavan projektin työmäärään. Työn päätavoite oli kvantitatiivisesti tutkia yllä mainitun havainnon oikeellisuus. Tavoitteen saavuttamiseksi suunniteltiin ohjelmistoarkkitehtuurisuunnittelun mittaristo kuvaamaan kyseisen järjestelmän alijärjestelmien arkkitehtuuria ja luotiin kaksi suunniteltua mittaristoa käyttävää, työmäärää (komponentin suunnittelu-, toteutus- ja testausaikojen summa) arvioivaa mallia, joista toinen on lineaarinen ja toinen epälineaarinen. Näiden mallien kertoimet sovitettiin optimoimalla niiden arvot epälineaarista gloobaalioptimointimenetelmää, differentiaalievoluutioalgoritmia, käyttäen, niin että mallien antamat arvot vastasivat parhaiten mitattua työmäärää sekä kaikilla ominaisuuksilla eli attribuuteilla että vain osalla niistä (yksi jätettiin vuorotellen pois). Kun arkkitehtuurikompenttien väliset kytkennät jätettiin malleista pois, mitattujen ja arvoitujen työmäärien välinen ero (ilmaistuna virheenä) kasvoi eräässä tapauksessa 367 % entisestä tarkoittaen sitä, että näin muodostettu malli vastasi toteutusaikoja huonosti annetulla ainestolla. Tämä oli suurin havaitu virhe kaikkien poisjätettyjen ominaisuuksien kesken. Saadun tuloksen perusteella päätettiin, että kyseisen järjestelmän toteutusajat ovat vahvasti riippuvaisia kytkentöjen määrästä, ja näin ollen kytkentöjen määrä oli mitä todennäköisemmin kaikista tärkein työmäärään vaikuttava tekijä tutkitun järjestelmän arkkitehtuurisuunnittelussa.
Resumo:
Työn päätavoitteena oli selvittää hinnan ja kilpailutilanteen vaikutusta matkaviestinnän diffuusioon. Työn empiirinen osuus tarkasteli matkapuhelinliittymien hinnan vaikutusta liittymien diffuusioon sekä sitä, miten alan kilpailu on vaikuttanut matkaviestinnän hintatasoon. Työssä analysoitiin myös matkaviestinnän kilpailutilannetta Suomen markkinoilla. Tutkimuksen empiirinen aineisto kerättiin toissijaisista lähteistä, esimerkiksi EMC-tietokannasta. Tutkimus oli luonteeltaan kvantitatiivinen.Empiirisessä osassa käytetyt mallit oli muodostettu aikaisempien tutkimuksien perusteella. Regressioanalyysiä käytettiin arvioitaessa hinnan vaikutusta diffuusionopeuteen ja mahdollisten omaksujien määrään. Regressioanalyysissä sovellettiin ei-lineaarista mallia.Tutkimustulokset osoittivat, että tasaisesti laskevilla matkapuhelinliittymien sekä matkapuhelimien hinnoilla ei ole merkittävää vaikutusta matkaviestinnän diffuusioon. Myöskään kilpailutilanne ei ole vaikuttanut paljon matkaviestinnän yleiseen hintatasoon. Työn tulosten perusteella voitiin antaa myös muutamia toimenpide-ehdotuksia jatkotutkimuksia varten.
Resumo:
The main subject of this master's thesis was predicting diffusion of innovations. The prediction was done in a special case: product has been available in some countries, and based on its diffusion in those countries the prediction is done for other countries. The prediction was based on finding similar countries with Self-Organizing Map~(SOM), using parameters of countries. Parameters included various economical and social key figures. SOM was optimised for different products using two different methods: (a) by adding diffusion information of products to the country parameters, and (b) by weighting the country parameters based on their importance for the diffusion of different products. A novel method using Differential Evolution (DE) was developed to solve the latter, highly non-linear optimisation problem. Results were fairly good. The prediction method seems to be on a solid theoretical foundation. The results based on country data were good. Instead, optimisation for different products did not generally offer clear benefit, but in some cases the improvement was clearly noticeable. The weights found for the parameters of the countries with the developed SOM optimisation method were interesting, and most of them could be explained by properties of the products.