Στην παρούσα διπλωματική εργασία θα γίνει μελέτη και εφαρμογή μεθόδων επίλυσης του προβλήματος αναγνώρισης γεωμετρικών χαρακτηριστικών ανθρώπινων ερυθρών αιμοσφαιρίων από προσομοιωμένες εικόνες σκέδασης ΗΜ ακτινοβολίας ενός He-Ne laser 632.8 μm. Στο πρώτο κεφάλαιο γίνεται μια εισαγωγή στις ιδιότητες και τα χαρακτηριστικά του ερυθροκυττάρου καθώς, επίσης, παρουσιάζονται διάφορες ανωμαλίες των ερυθροκυττάρων και οι μέχρι στιγμής χρησιμοποιούμενοι τρόποι ανίχνευσής των. Στο δεύτερο κεφάλαιο της εργασίας γίνεται μια εισαγωγή στις ιδιότητες της ΗΜ ακτινοβολίας, περιγράφεται το φαινόμενο της σκέδασης και παρουσιάζεται το ευθύ πρόβλημα σκέδασης ΗΜ ακτινοβολίας ανθρώπινων ερυθροκυττάρων. Το τρίτο κεφάλαιο αποτελείται από δύο μέρη. Στο πρώτο μέρος γίνεται εκτενής ανάλυση της θεωρίας των τεχνητών νευρωνικών δικτύων και περιγράφονται τα νευρωνικά δίκτυα ακτινικών συναρτήσεων RBF. Στη συνέχεια, αναφέρονται οι μέθοδοι εξαγωγής παραμέτρων και, πιο συγκεκριμένα, δίνεται το θεωρητικό και μαθηματικό υπόβαθρο των μεθόδων που χρησιμοποιήθηκαν οι οποίες είναι ο αλογόριθμος Singular Value Decomposition (SVD), o Angular Radial μετασχηματισμός (ART) και φίλτρα Gabor. Στο δεύτερο μέρος περιγράφεται η επίλυση του αντίστροφου προβλήματος σκέδασης. Παρουσιάζεται η μεθοδολογία της διαδικασίας επίλυσης όπου εφαρμόστηκαν ο αλογόριθμος συμπίεσης εικόνας SVD, o περιγραφέας σχήματος ART και ο περιγραφέας υφής με φίλτρα Gabor για την εύρεση των γεωμετρικών χαρακτηριστικών και νευρωνικό δίκτυο ακτινικών συναρτήσεων RBF για την ταξινόμηση των ερυθροκυττάρων. Στο τέταρτο και τελευταίο κεφάλαιο γίνεται δοκιμή και αξιολόγηση της μεθόδου και συνοψίζονται τα αποτελέσματα και τα συμπεράσματα που εξήχθησαν κατά τη διάρκεια της εκπόνησης αυτής της διπλωματικής. / In this thesis we study and implement methods of estimating the geometrical features of the human red blood cell from a set of simulated light scattering images produced by a He-Ne laser beam at 632.8 μm. Ιn first chapter an introduction to the properties and the characteristics of red blood cells are presented. Furthermore, we describe various abnormalities of erythrocytes and the until now used ways of detection. In second chapter the properties of electromagnetic radiation and the light scattering problem of EM radiation from human erythrocytes are presented. The third chapter consists of two parts. In first part we analyse the theory of neural networks and we describe the radial basis function neural network. Then, we describe the theoritical and mathematical background of the methods that we use for feature extraction which are Singular Value Decomposition (SVD), Angular Radial Transform and Gabor filters. In second part the solution of the inverse problem of light scattering is described. We present the methodology of the solution process in which we implement a Singular Value Decomposition approach, a shape descriptor with Angular Radial Transform and a homogenous texture descriptor which uses Gabor filters for the estimation of the geometrical characteristics and a RBF neural network for the classification of the erythrocytes. In the forth and last chapter the described methods are evaluated and we summarise the experimental results and conclusions that were extracted from this thesis.

Neste trabalho, um novo enfoque para detecção e diagnóstico de falhas (DDF) em robôs manipuladores é apresentado. Um robô com falhas pode causar sérios danos e pode colocar em risco o pessoal presente no ambiente de trabalho. Geralmente, os pesquisadores têm proposto esquemas de DDF baseados no modelo matemático do sistema. Contudo, erros de modelagem podem ocultar os efeitos das falhas e podem ser uma fonte de alarmes falsos. Aqui, duas redes neurais artificiais são utilizadas em um sistema de DDF para robôs manipuladores. Um perceptron multicamadas treinado por retropropagação do erro é usado para reproduzir o comportamento dinâmico do manipulador. As saídas do perceptron são comparadas com as variáveis medidas, gerando o vetor de resíduos. Em seguida, uma rede com função de base radial é usada para classificar os resíduos, gerando a isolação das falhas. Quatro algoritmos diferentes são empregados para treinar esta rede. O primeiro utiliza regularização para reduzir a flexibilidade do modelo. O segundo emprega regularização também, mas ao invés de um único termo de penalidade, cada unidade radial tem um regularização individual. O terceiro algoritmo emprega seleção de subconjuntos para selecionar as unidades radiais a partir dos padrões de treinamento. O quarto emprega o mapa auto-organizável de Kohonen para fixar os centros das unidades radiais próximos aos centros dos aglomerados de padrões. Simulações usando um manipulador com dois graus de liberdade e um Puma 560 são apresentadas, demostrando que o sistema consegue detectar e diagnosticar corretamente falhas que ocorrem em conjuntos de padrões não-treinados. / In this work, a new approach for fault detection and diagnosis in robotic manipulators is presented. A faulty robot could cause serious damages and put in risk the people involved. Usually, researchers have proposed fault detection and diagnosis schemes based on the mathematical model of the system. However, modeling errors could obscure the fault effects and could be a false alarm source. In this work, two artificial neural networks are employed in a fault detection and diagnosis system to robotic manipulators. A multilayer perceptron trained with backpropagation algorithm is employed to reproduce the robotic manipulator dynamical behavior. The perceptron outputs are compared with the real measurements, generating the residual vector. A radial basis function network is utilized to classify the residual vector, generating the fault isolation. Four different algorithms have been employed to train this network. The first utilizes regularization to reduce the flexibility of the model. The second employs regularization too, but instead of only one penalty term, each radial unit has a individual penalty term. The third employs subset selection to choose the radial units from the training patterns. The forth algorithm employs the Kohonen’s self-organizing map to fix the radial unit center near to the cluster centers. Simulations employing a two link manipulator and a Puma 560 manipulator are presented, demonstrating that the system can detect and isolate correctly faults that occur in nontrained pattern sets.

Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Dissertacao Gustavo Araujo.pdf: 2606649 bytes, checksum: efb1a5ded768b058f25d23ee8967bd38 (MD5) Previous issue date: 2014-04-28 / In this work the main objective is to present the development of learning algorithms for online application for the solution of algebraic Hamilton-Jacobi-Bellman equation. The concepts covered are focused on developing the methodology for control systems, through techniques that aims to design online adaptive controllers to reject noise sensors, parametric variations and modeling errors. Concepts of neurodynamic programming and reinforcement learning are are discussed to design algorithms where the context of a given operating point causes the control system to adapt and thus present the performance according to specifications design. Are designed methods for online estimation of adaptive critic focusing efforts on techniques for gradient estimating of the environment value function. / Neste trabalho o principal objetivo é apresentar o desenvolvimento de algoritmos de aprendizagem para execução online para a solução da equação algébrica de Hamilton-Jacobi-Bellman. Os conceitos abordados se concentram no desenvolvimento da metodologia para sistemas de controle, por meio de técnicas que tem como objetivo o projeto online de controladores adaptativos são projetados para rejeitar ruídos de sensores, variações paramétricas e erros de modelagem. Conceitos de programação neurodinâmica e aprendizagem por reforço são abordados para desenvolver algoritmos onde a contextualização de determinado ponto de operação faz com que o sistema de controle se adapte e, dessa forma, apresente o desempenho de acordo com as especificações de projeto. Desenvolve-se métodos para a estimação online do crítico adaptativo concentrando os esforços em técnicas de estimação do gradiente da função valor do ambiente.

Dans le domaine d’optimisation de forme de structures, la réduction des coûts et l’amélioration des produits sont des défis permanents à relever. Pour ce faire, le procédé de mise en forme doit être optimisé. Optimiser le procédé revient alors à résoudre un problème d’optimisation. Généralement ce problème est un problème d’optimisation multicritère très coûteux en terme de temps de calcul, où on cherche à minimiser plusieurs fonctions coût en présence d’un certain nombre de contraintes. Pour résoudre ce type de problème, on a développé un algorithme robuste, efficace et fiable. Cet algorithme, consiste à coupler un algorithme de capture de front de Pareto (NBI ou NNCM) avec un métamodèle (RBF), c’est-à-dire des approximations des résultats des simulations coûteuses. D’après l’ensemble des résultats obtenus par cette approche, il est intéressant de souligner que la capture de front de Pareto génère un ensemble des solutions non dominées. Pour savoir lesquelles choisir, le cas échéant, il est nécessaire de faire appel à des algorithmes de sélection, comme par exemple Nash et Kalai-Smorodinsky. Ces deux approches, issues de la théorie des jeux, ont été utilisées pour notre travail. L’ensemble des algorithmes sont validés sur deux cas industriels proposés par notre partenaire industriel. Le premier concerne un modèle 2D du fond de la canette (elasto-plasticité) et le second est un modèle 3D de la traverse (élasticité linéaire). Les résultats obtenus confirment l’efficacité de nos algorithmes développés. / One of the current challenges in the domain of the multiobjective shape optimization is to reduce the calculation time required by conventional methods. The high computational cost is due to the high number of simulation or function calls required by these methods. Recently, several studies have been led to overcome this problem by integratinga metamodel in the overall optimization loop. In this thesis, we perform a coupling between the Normal Boundary Intersection -NBI- algorithm and The Normalized Normal constraint Method -NNCM- algorithm with Radial Basis Function -RBF- metamodel in order to have asimple tool with a reasonable calculation time to solve multicriteria optimization problems. First, we apply our approach to academic test cases. Then, we validate our method against two industrial cases, namely, shape optimization of the bottom of a can undergoing nonlinear elasto-plastic deformation and an optimization of an automotive twist beam. Then, in order to select solutions among the Pareto efficient ones, we use the same surrogate approach to implement a method to compute Nash and Kalai-Smorodinsky equilibria.

Dans l’industrie de fabrication mécanique et notamment pour l’utilisation des centres d’usinage haute vitesse, la connaissance des propriétés dynamiques du système broche-outil-pièce en opération est d’une grande importance. L’accroissement des performances des machines-outils et des outils de coupe a œuvré au développement de ce procédé compétitif. D’innombrables travaux ont été menés pour accroître les performances et les remarquables avancées dans les matériaux, les revêtements des outils coupants et les lubrifiants ont permis d’accroître considérablement les vitesses de coupe tout en améliorant la qualité de la surface usinée. Cependant, l’utilisation rationnelle de cette technologie est encore fortement pénalisée par les lacunes dans la connaissance de la coupe, que ce soit au niveau microscopique des interactions fines entre l’outil et la matière coupée, aussi bien qu’au niveau macroscopique intégrant le comportement de la cellule élémentaire d’usinage, si bien que le comportement dynamique en coupe garde encore une grande part de questionnement et exige de l’utilisateur un bon niveau de savoir-faire et parfois d’empirisme pour exploiter au mieux les capacités des moyens de production. Le fonctionnement des machines d’usinage engendre des vibrations qui sont souvent la cause des dysfonctionnements et accélère l’usure des composantes mécaniques (roulements) et outils. Ces vibrations sont une image des efforts internes des systèmes, d’où l’intérêt d’analyser les grandeurs mécaniques vibratoires telle que la vitesse ou l’accélération vibratoire. Ces outils sont indispensables pour une maintenance moderne dont l’objectif est de réduire les coûts liés aux pannes / In machining field, chatter phenomenon takes a lot of interest because manufacturing enterprises are turning to the automation system and the development of reliable and robust monitoring system to provide increased productivity, improved part quality and reduced costs. Chatter occurrence has several negatives effects: a) Poor surface quality, b) Unacceptable inaccuracy, c) Excessive noise, d) Machine tool damage, e) Reduced material removal rate, f) Increase costs in terms of production time, g) Waste of material, h) Environmental impact in terms of materials and energy. Moreover, chatter monitoring is not an easy task for various reasons. Firstly, the non linearity of machining processes and the time-varying of systems complicate this task. Secondly, the sensitivity and the dependency of acquired signals from sensors on different factors, such as machining condition, cutting tool geometry and workpiece material. Thirdly, at high rotating speeds, the gyroscopic effects on the spindle dynamics in addition to the centrifugal force on the bearings and thermal effects become more relevant thus affecting the stability of the system. For these reasons, demands for an advanced automatic chatter detection and monitoring system for optimizing and controlling machining processes becomes a topic of enormous interest. Several researches in this field are performed. Advanced monitoring and detection methods are developed mostly relying on time, frequency and time-frequency analysis. In order to detect chatter in milling centers, three new methods are studied and developed using advanced techniques of signal processing and exploiting cyclostationarity property of signals acquired

The present work focuses on the two areas of investigation: system identification of helicopter and design of controller for the helicopter. Helicopter system identification, the first subject of investigation in this thesis, can be described as the extraction of system characteristics/dynamics from measured flight test data. Wind tunnel experimental data suffers from scale effects and model deficiencies. The increasing need for accurate models for the design of high bandwidth control system for helicopters has initiated a renewed interest in and a more active use of system identification. Besides, system identification is likely to become mandatory in the future for model validation of ground based helicopter simulators. Such simulators require accurate models in order to be accepted by pilots and regulatory authorities like Federal Aviation Regulation for realistic complementary helicopter mission training. Two approaches are widely used for system identification, namely, black box and gray box approach. In the black-box approach, the relationship between input-output data is approximated using nonparametric methods such as neural networks and in such a case, internal details of the system and model structure may not be known. In the gray box approach, parameters are estimated after defining the model structure. In this thesis, both black box and gray box approaches are investigated. In the black box approach, in this thesis, a comparative study and analysis of different Recurrent Neural Networks(RNN) for the identification of helicopter dynamics using flight data is investigated. Three different RNN architectures namely, Nonlinear Auto Regressive eXogenous input(NARX) model, neural network with internal memory known as Memory Neuron Networks(MNN)and Recurrent MultiLayer perceptron (RMLP) networks are used to identify dynamics of the helicopter at various flight conditions. Based on the results, the practical utility, advantages and limitations of the three models are critically appraised and it is found that the NARX model is most suitable for the identification of helicopter dynamics. In the gray box approach, helicopter model parameters are estimated after defining the model structure. The identification process becomes more difficult as the number of degrees-of-freedom and model parameters increase. To avoid the drawbacks of conventional methods, neural network based techniques, called the delta method is investigated in this thesis. This method does not require initial estimates of the parameters and the parameters can be directly extracted from the flight data. The Radial Basis Function Network(RBFN)is used for the purpose of estimation of parameters. It is shown that RBFN is able to satisfactorily estimate stability and control derivatives using the delta method. The second area of investigation addressed in this thesis is the control of helicopter in flight. Helicopter requires use of a control system to achieve satisfactory flight. Designing a classical controller involves developing a nonlinear model of the helicopter and extracting linearized state space matrices from the nonlinear model at various flight conditions. After examining the stability characteristics of the helicopter, the desired response is obtained using a feedback control system. The scheduling of controller gains over the entire envelope is used to obtain the desired response. In the present work, a helicopter having a soft inplane four bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is considered. For this helicopter, a mathematical model and also a model based on neural network (using flight data) has been developed. As a precursor, a feed back controller, the Stability Augmentation System(SAS), is designed using linear quadratic regulator control with full state feedback and LQR with out put feedback approaches. SAS is designed to meet the handling qualities specification known as Aeronautical Design Standard ADS-33E-PRF. The control gains have been tuned with respect to forward speed and gain scheduling has been arrived at. The SAS in the longitudinal axis meets the requirement of the Level1 handling quality specifications in hover and low speed as well as for forward speed flight conditions. The SAS in the lateral axis meets the requirement of the Level2 handling quality specifications in both hover and low speed as well as for forward speed flight conditions. Such conventional design of control has served useful purposes, however, it requires considerable flight testing which is time consuming, to demonstrate and tune these control law gains. In modern helicopters, the stringent requirements and non-linear maneuvers make the controller design further complicated. Hence, new design tools have to be explored to control such helicopters. Among the many approaches in adaptive control, neural networks present a potential alternative for modeling and control of nonlinear dynamical systems due to their approximating capabilities and inherent adaptive features. Furthermore, from a practical perspective, the massive parallelism and fast adaptability of neural network implementations provide more incentive for further investigation in problems involving dynamical systems with unknown non-linearity. Therefore, adaptive control approach based on neural networks is proposed in this thesis. A neural network based Feedback Error Neural adaptive Controller(FENC) is designed for a helicopter. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non-linearity and parameter un-certainties. Nonlinear Auto Regressive eXogenous input(NARX)neural network architecture is used to approximate the control law and the controller network parameters are adapted using updated rules Lyapunov synthesis. An offline (finite time interval)and on-line adaptation strategy is used to approximate system uncertainties. The results are validated using simulation studies on helicopter undergoing an agile maneuver. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications. Even though the tracking error is less in FENC scheme, the control effort required to follow the command is very high. To overcome these problems, a Direct Adaptive Neural Control(DANC)scheme to track the rate command signal is presented. The neural controller is designed to track rate command signal generated using the reference model. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using back propagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval)network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller is compared with feedback error learning neural controller. The performance of the controller has been validated at various flight conditions. The theoretical results are validated using simulation studies based on a nonlinear six degree-of-freedom helicopter undergoing an agile maneuver. Realistic gust and sensor noise are added to the system to study the disturbance rejection properties of the neural controllers. To investigate the on-line learning ability of the proposed neural controller, different fault scenarios representing large model error and control surface loss are considered. The performances of the proposed DANC scheme is compared with the FENC scheme. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications.

This study proposes novel optimization methodologies for the optimization of problems that reveal non-physical step discontinuities. More specifically, it is proposed to use gradient-only techniques that do not use any zeroth order information at all for step discontinuous problems. A step discontinuous problem of note is the shape optimization problem in the presence of remeshing strategies, since changes in mesh topologies may - and normally do - introduce non-physical step discontinuities. These discontinuities may in turn manifest themselves as non-physical local minima in which optimization algorithms may become trapped. Conventional optimization approaches for step discontinuous problems include evolutionary strategies, and design of experiment (DoE) techniques. These conventional approaches typically rely on the exclusive use of zeroth order information to overcome the discontinuities, but are characterized by two important shortcomings: Firstly, the computational demands of zero order methods may be very high, since many function values are in general required. Secondly, the use of zero order information only does not necessarily guarantee that the algorithms will not terminate in highly unfit local minima. In contrast, the methodologies proposed herein use only first order information, rather than only zeroth order information. The motivation for this approach is that associated gradient information in the presence of remeshing remains accurately and uniquely computable, notwithstanding the presence of discontinuities. From a computational effort point of view, a gradient-only approach is of course comparable to conventional gradient based techniques. In addition, the step discontinuities do not manifest themselves as local minima. / Thesis (PhD)--University of Pretoria, 2010. / Mechanical and Aeronautical Engineering / unrestricted

In the 21st century, information is the new currency. With the omnipresence of devices connected to the internet, humanity can instantly avail any information. However, there are certain are cybercrime groups which steal the information. An Intrusion Detection System (IDS) monitors a network for suspicious activities and alerts its owner about an undesired intrusion. These commercial IDS’es react after detecting intrusion attempts. With the cyber attacks becoming increasingly complex, it is expensive to wait for the attacks to happen and respond later. It is crucial for network owners to employ IDS’es that preemptively differentiate a harmless data request from a malicious one. Machine Learning (ML) can solve this problem by recognizing patterns in internet traffic to predict the behaviour of network users. This project studies how effectively Radial Basis Function Neural Network (RBFN) with Deep Learning Architecture can impact intrusion detection. On the basis of the existing framework, it asks how well can an RBFN predict malicious intrusive attempts, especially when compared to contemporary detection practices.Here, an RBFN is a multi-layered neural network model that uses a radial basis function to transform input traffic data. Once transformed, it is possible to separate the various traffic data points using a single straight line in extradimensional space. The outcome of the project indicates that the proposed method is severely affected by limitations. E.g. the model needs to be fine tuned over several trials to achieve a desired accuracy. The results of the implementation show that RBFN is accurate at predicting various cyber attacks such as web attacks, infiltrations, brute force, SSH etc, and normal internet behaviour on an average 80% of the time. Other algorithms in identical testbed are more than 90% accurate. Despite the lower accuracy, RBFN model is more than 94% accurate at recording specific kinds of attacks such as Port Scans and BotNet malware. One possible solution is to restrict this model to predict only malware attacks and use different machine learning algorithm for other attacks. / I det 21: a århundradet är information den nya valutan. Med allnärvaro av enheter anslutna till internet har mänskligheten tillgång till information inom ett ögonblick. Det finns dock vissa grupper som använder metoder för att stjäla information för personlig vinst via internet. Ett intrångsdetekteringssystem (IDS) övervakar ett nätverk för misstänkta aktiviteter och varnar dess ägare om ett oönskat intrång skett. Kommersiella IDS reagerar efter detekteringen av ett intrångsförsök. Angreppen blir alltmer komplexa och det kan vara dyrt att vänta på att attackerna ska ske för att reagera senare. Det är avgörande för nätverksägare att använda IDS:er som på ett förebyggande sätt kan skilja på oskadlig dataanvändning från skadlig. Maskininlärning kan lösa detta problem. Den kan analysera all befintliga data om internettrafik, känna igen mönster och förutse användarnas beteende. Detta projekt syftar till att studera hur effektivt Radial Basis Function Neural Networks (RBFN) med Djupinlärnings arkitektur kan påverka intrångsdetektering. Från detta perspektiv ställs frågan hur väl en RBFN kan förutsäga skadliga intrångsförsök, särskilt i jämförelse med befintliga detektionsmetoder.Här är RBFN definierad som en flera-lagers neuralt nätverksmodell som använder en radiell grundfunktion för att omvandla data till linjärt separerbar. Efter en undersökning av modern litteratur och lokalisering av ett namngivet dataset användes kvantitativ forskningsmetodik med prestanda indikatorer för att utvärdera RBFN: s prestanda. En Random Forest Classifier algorithm användes också för jämförelse. Resultaten erhölls efter en serie finjusteringar av parametrar på modellerna. Resultaten visar att RBFN är korrekt när den förutsäger avvikande internetbeteende i genomsnitt 80% av tiden. Andra algoritmer i litteraturen beskrivs som mer än 90% korrekta. Den föreslagna RBFN-modellen är emellertid mycket exakt när man registrerar specifika typer av attacker som Port Scans och BotNet malware. Resultatet av projektet visar att den föreslagna metoden är allvarligt påverkad av begränsningar. T.ex. så behöver modellen finjusteras över flera försök för att uppnå önskad noggrannhet. En möjlig lösning är att begränsa denna modell till att endast förutsäga malware-attacker och använda andra maskininlärnings-algoritmer för andra attacker.

<p>Математички модели за естимацију тешко мерљивих величина називају<br />се софт сензорима. Процес формирања софт сензора није тривијалан и<br />квалитет естимације тешко мерљиве величине директно зависи од<br />начина формирања. Недостаци постојећих алгоритама за формирање<br />спречавају аутоматску конфигурацију софт сензора. У овом раду су<br />реализовани нови алгоритми који имају за сврху аутоматизацију<br />конфигурације софт сензора. Реализовани алгоритми решавају<br />проблеме проналаска оптималног сета улаза у софт сензор и кашњења<br />сваког од њих као и одабира структуре и начина обуке софт сензора<br />заснованих на вештачким неуронским мрежама са радијално базираним<br />функцијама.</p> / <p>Matematički modeli za estimaciju teško merljivih veličina nazivaju<br />se soft senzorima. Proces formiranja soft senzora nije trivijalan i<br />kvalitet estimacije teško merljive veličine direktno zavisi od<br />načina formiranja. Nedostaci postojećih algoritama za formiranje<br />sprečavaju automatsku konfiguraciju soft senzora. U ovom radu su<br />realizovani novi algoritmi koji imaju za svrhu automatizaciju<br />konfiguracije soft senzora. Realizovani algoritmi rešavaju<br />probleme pronalaska optimalnog seta ulaza u soft senzor i kašnjenja<br />svakog od njih kao i odabira strukture i načina obuke soft senzora<br />zasnovanih na veštačkim neuronskim mrežama sa radijalno baziranim<br />funkcijama.</p> / <p>Mathematical models that are used for estimation of variables that can not be<br />measured in real time are called soft sensors. Creation of soft sensor is a<br />complex process and quality of estimation depends on the way soft sensor is<br />created. Restricted applicability of existing algorithms is preventing automatic<br />configuration of soft sensors. This paper presents new algorithms that are<br />providing automatic configuration of soft sensors. Presented algorithms are<br />capable of determing optimal subset of soft sensor inputs and their time<br />delays, as well as optimal architecture and automatic training of the soft<br />sensors that are based on artificial radial basis function networks.</p>

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