Neuro-fuzzy modeling is an efficient computing paradigm for system modeling problems. It mainly integrates two well-known approaches, neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. We propose in this thesis two self-constructing rule generation methods, i.e., similarity-based rule generation (SRG) and similarity-and-merge-based rule generation (SMRG), and one hybrid learning algorithm (HLA) for structure identification and parameter identification, respectively, of neuro-fuzzy modeling. SRG and SMRG group the input-output training data into a set of fuzzy clusters incrementally based on similarity tests on the input and output spaces. Membership functions associated with each cluster are defined according to statistical means and deviations of the data points included in the cluster. Additionally, SMRG employs a merging mechanism to merge similar clusters dynamically. Then a zero-order or first-order TSK-type fuzzy IF-THEN rule is extracted from each cluster to form an initial fuzzy rule-base which can be directly employed for fuzzy reasoning or be further refined in the next phase of parameter identification. Compared with other methods, both our SRG and SMRG have advantages of generating fuzzy rules quickly, matching membership functions closely with the real distribution of the training data points, and avoiding the generation of the whole set of clusters from the scratch when new training data are considered. Besides, SMRG supports a more reasonable and quick mechanism for cluster merging to alleviate the problems of data-input-order bias and redundant clusters, which are encountered in SRG and other incremental clustering approaches. To refine the fuzzy rules obtained in the structure identification phase, a zero-order or first-order TSK-type fuzzy neural network is constructed accordingly in the parameter identification phase. Then, we develop a HLA composed by a recursive SVD-based least squares estimator and the gradient descent method to train the network. Our HLA has the advantage of alleviating the local minimal problem. Besides, it learns faster, consumes less memory, and produces lower approximation errors than other methods. To verify the practicability of our approaches, we apply them to the applications of function approximation and classification. For function approximation, we apply our approaches to model several nonlinear functions and real cases from measured input-output datasets. For classification, our approaches are applied to a problem of human object segmentation. A fuzzy self-clustering algorithm is used to divide the base frame of a video stream into a set of segments which are then categorized as foreground or background based on a combination of multiple criteria. Then, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network which is constructed with the fuzzy rules previously obtained and is trained by our proposed HLA. Experimental results show that our approaches can improve the accuracy of human object identification in video streams and work well even when the human object presents no significant motion in an image sequence.

Gas Metal Arc Welding (GMAW) is one of the main methods used to join structural members. One of the largest challenges involved in production of welds is ensuring the quality of the weld. One of the main factors attributing to weld quality is penetration depth. Automatic control of the welding process requires non-contact, non-destructive sensors that can operate in the presence of high temperatures and electrical noise found in the welding environment. Inspection using laser generation and electromagnetic acoustic transducer (EMAT) reception of ultrasound was found to satisfy these conditions. Using this technique, the time of flight of the ultrasonic wave is measured and used to calculate penetration depth. Previous works have shown that penetration depth measurement performance is drastically reduced when performed during welding. This work seeks to realize in-process penetration depth measurement by compensating for errors caused by elevated temperature. Neuro-fuzzy models are developed that predict penetration depth based on in-process time of flight measurements and the welding process input. Two scenarios are considered in which destructive penetration depth measurements are or are not available for model training. Results show the two scenarios are successful. When destructive measurements are unavailable, model error is comparable to that of offline ultrasonic measurements. When destructive measurements are available, measurement error is reduced by 50% compared to offline ultrasonic measurements. The two models can be effectively applied to permit in-process penetration depth measurements for the purpose of real-time monitoring and control. This will reduce material, production time, and labor costs and increase the quality of welded parts.

Σήμερα η ανάγκη για δημιουργία ποιοτικών και φθίνων και ανταγωνιστικών βιομηχανικών προϊόντων έχει σαν αποτέλεσμα να χρειαζόμαστε αυτοματισμούς και αυτόματο έλεγχο ηλεκτρικών μηχανών με μεγάλη ακρίβεια και αδιάλειπτη λειτουργία απο διαταραχές του περιβάλλοντος . Επιπλέον το ενεργειακό πρόβλημα που είναι από τα σπουδαιότερα προβλήματα του πλανήτη και του ανθρώπου σήμερα οδηγούν στην ανάγκη ελαχιστοποίησης των ενεργειακών απωλειών με αποτέλεσμα συνήθως να επιθυμούμε λειτουργία των ηλεκτρομηχανικών συστημάτων με μηδενική κατανάλωση/ παραγωγή άεργου ισχύος . Η Ηλεκτρική μηχανή συνεχούς ρεύματος με διέγερση σε σειρά χρησιμοποιείται λόγω των ιδιαίτερων χαρακτηριστικών της σε πολλές εφαρμογές που χρειάζονται υψηλή ροπή εκκίνησης όπως ανυψωτικά μηχανήματα, σιδηροδρομικά οχήματα. Οι ανορθωτές με ελεγχόμενη έναυση αλλά και σβέση κατά PWM προτιμούνται έναντι των διόδων και των θυρίστορς γιατί μας δίνουν περισσότερες δυνατότητες ελέγχου . Η Μοντελοποίηση , σχεδιασμός συστήματος ελέγχου, ευστάθειας του συστήματος για την μηχανή συνεχούς ρεύματος οδηγούμενης από τριφασικό ανορθωτή ερευνάται. Γίνεται εξαγωγή του μοντέλου, στο τριφασικό σύστημα και στο πλαίσιο park, με βάση την δυναμική ανάλυση Εuler -Lagrange . Για την εξαγωγή του μοντέλου γίνεται ακόμα χρήση του Averaging Analysis . Ανάλυση που βασίζεται στην παθητικότητα μάς δείχνει ότι το σύστημα είναι ευσταθές πεπερασμένης εισόδου-πεπερασμένης κατάστασης. Οι ελεγκτές που αναπτύσσονται σε αυτή τη εργασία είναι ο ασαφής ελεγκτής, ο νευροασαφής που χρησιμοποιούνται ευρέως σε μη γραμμικά συστήματα στην βιομηχανία, ο PI σειριακός (cascade) ελεγκτής που συνηθίζεται στις ηλεκτρικές μηχανές. Τέλος επιχειρείται ο σχεδιασμός ενός PI ελεγκτή με υπολογισμό κερδών από νευροασαφή εκτιμητή. Ο ασαφής ελεγκτής σχεδιάζεται με πρόβλεψη σφάλματος και επιτυγχάνει πολύ καλή ρύθμιση των στροφών και καλό έλεγχο στης αέργου ισχύος, εξομοιώσεις επιβεβαιώνουν την απόδοση του ελεγκτή. Ομοίως ισχύουν για τον νευροασαφή ελεγκτή με το πλεονέκτημα μικρότερου υπολογιστικού χρόνου αλλά μειονεκτεί μεγαλύτερης εμφάνισης ενός μόνιμου σφάλματος. Ο PI σειριακός (cascade) επιτυγχάνει άριστη ρύθμιση αέργου ισχύος και καλή ρύθμιση στροφών ενώ ο PI casacde- Anfis μας δίνει ελαφριά καλύτερα αποτελέσματα αλλά αφήνει αρκετές δυνατότητες για περαιτέρω σχεδιασμό και έρευνα. Επίσης γίνεται κάποια ανάλυση για εξαγωγή συμπερασμάτων ευστάθειας και σύγκλισης για τα σύστημα κλειστού βρόχου. Τα αποτελέσματα τα επιβεβαιώνουμε και τα συγκρίνουμε μέσω εξομοιώσεων. / Nowadays , the demand for precise control in industrial applications require the design and development of advanced controllers. Also the energy problem which is one of the most important global problems lead to the need of high energy efficient systems. In industrial applications in most cases ,due to the energy problem , we desire operation with unity power factor. The dc series connected motor is preferred in many application such as railway and levitating systems due to its high starting torque. We choose the 3 -phase pulse width modulation rectifier because of its many capabilities comparison with thyristor rectifiers. Modeling, control design and stability analysis of series connected dc motor fed by three-phase PWM ac/dc voltage converter are investigated. The designed controllers are fuzzy , neuro fuzzy, PI cascade and Anfis- pi cascade controller. The model is obtained via Euler -Lagrange dynamic analysis. Also we used the averaging analysis in order to determine the dynamic model of the system in a-b-c frame and d-q park's frame. We prove the ISS stability of the open loop system based on passivity analysis. The fuzzy use a predictive logic based on the acceleration of the motor, we result excellent precise control of angular velocity and a satisfied control of reactive power. Neuro Fuzzy controller has the same effectiveness with less computational effort but has a possibility to occur a small permanent error in angular velocity. PI cascade controller has as a result a excellent response at reactive power and good response in angular velocity with more less computational effort. ANFIS -PI cascade controller have a bit better results from PI-cascade controllers but it leaves hopes for more optimum designs in feature. Furthermore there are some stability and convergence analysis for the closed loop system. Simulation results verify the effectiveness of each controller for comparison.

Baigiamajame magistro darbe nagrinėjamos neraiškiųjų verslo taisyklių naudojimo informacinėse sistemose problemos, „minkštųjų skaičiavimų“ intelektinėse informacinėse sistemose problematika, neuroninių-neraiškiųjų sistemų principai. Išnagrinėti pagrindiniai neraiškiosios logikos dėsniai, kuriais remiantis naudojamos neraiskiosios verslo taisyklės intelektinėse informacinėse sistemose. Pateiktas būdas, kaip neuroninės-neraiškiosios sistemos gali būti naudojamos verslo taisyklių sistemose naudojant RuleML, taisyklių žymėjimo kalbos, standartą. Baigiamajame darbe aprašomas eksperimentas, atliktas naudojant Matlab aplinką, XMLBeans taikomąją programą ir autoriaus sukurta neraiškaus išvedimo sistemos perkelimo į RuleML formatą taikomąją programą. Išnagrinėjus teorinius ir praktinius neuroninių-neraiškiųjų sistemų naudojimo aspektus, pateikiamos baigiamojo darbo išvados ir siūlymai. Darbą sudaro 5 dalys: įvadas, analitinė-metodinė dalis, eksperimentinė-tiriamoji dalis, išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 58 p. teksto be priedų, 30 iliustr., 30 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / This work investigates the problems of use of fuzzy business rules in information systems, „soft computing“ in intelligent information systems issues, neuro-fuzzy systems principles. Main fuzzy logic laws are considered, which are used as the basis of fuzzy business rules in intelligent information systems. Suggested an approach, based on RuleML standard, how neuro-fuzzy systems could be used together with business rules engines. This paper describes the experiment carried out using the Matlab environment, XMLBeans application and the author created application for fuzzy inference system migration to RuleML standard format. Structure: introduction, analysis , project, conclusions and suggestions, references. Thesis consist of: 58 p. text without appendixes, 30 pictures, 30 bibliographical entries. Appendixes included.

Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-05-19T18:15:12Z No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-23T20:32:54Z (GMT) No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-23T20:33:05Z (GMT) No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) / Made available in DSpace on 2017-05-25T14:21:19Z (GMT). No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) Previous issue date: 2016-04-29 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / This work deals with modeling applications, systematic and reliable optimization methodologies of global search, and other computational tools. It is expected that existing computational intelligence methods, encoded in an appropriate tool for the application of process engineering assisted by computer, can lead to useful numerical results for the modeling and optimization of different processes, including biotechnological processes (focus of this work). Thus, different types of methodologies suitable for computer applications, were studied here. The proposed methodologies were implemented and evaluated for the development and optimization of culture media for the fermentation process of Clostridium novyi type B, besides the fermentation process and enzymatic hydrolysis of bagasse associated with the production of bioethanol (1G and 2G). Thus, the potential application of these computational techniques was evaluated to biotechnological systems in different approaches. More specifically, it was performed: Classification of biotechnological systems ( "clustering") in kinetically similar regions to produce cellulosic ethanol (2G ethanol) using fuzzy logic; estimation by global search of kinetic parameters to an alcoholic fermentation model using Simmulated Annealing algorithm (SA) (Contributions to the thematic project FAPESP 2011 / 51902-9); formulation and optimization of economically viable culture media for Clostridium novyi type B using neuro-fuzzy data modeling followed by global search which maximize productivity, also utilizing SA algorithm as a search engine (this step of the project was conducted in partnership with the veterinary pharmaceutical company Vallée SA). The computational tools presented in this work were highly effective for modeling and optimization of the bioprocesses studied. / Este trabalho aborda aplicações de modelagem, metodologias sistemáticas e confiáveis de otimização por busca global, além de outras ferramentas computacionais. Espera-se que métodos de inteligência computacional existentes, codificados em uma ferramenta apropriada para a aplicação da engenharia de processos assistida por computador, resultem em resultados numéricos úteis para a modelagem e otimização de diferentes processos, incluindo-se os processos biotecnológicos (foco deste trabalho). Assim, diferentes tipos de metodologias, apropriadas para aplicações em computador, foram aqui estudadas. Os métodos propostos foram aplicados e avaliados ao desenvolvimento e otimização de meios de cultura para o processo fermentativo do microrganismo Clostridium novyi tipo B, além dos processos de fermentação alcoólica e hidrolise enzimática de bagaço de cana, associados à produção de bioetanol (1G e 2G). Desta forma, foi avaliado o potencial de aplicação destas técnicas computacionais aos sistemas biotecnológicos, em diversas abordagens. Mais especificamente, foram realizadas: classificação (“clustering”) de sistemas em regiões cineticamente semelhantes para a produção de etanol celulósico (Etanol 2G) utilizando lógica Fuzzy; estimação por busca global de parâmetros cinéticos do modelo para uma fermentação alcoólica utilizando o algoritmo Simmulated Annealing (SA) (Contribuições ao projeto temático FAPESP 2011/51902-9); formulação e otimização do meio de cultura economicamente viável para o Clostridium novyi tipo B utilizando a modelagem de dados por neuro-fuzzy seguido de busca global da composição de meio que maximize a produtividade utilizando também o algoritmo SA como ferramenta de busca global (esta etapa do projeto foi realizado em parceria com a empresa farmacêutica veterinária Vallée S.A). As ferramentas computacionais apresentadas neste trabalho se mostraram altamente efetivas para a modelagem e otimização dos bioprocessos estudados." / FAPESP: 2011/51902-9. / FAPESP: 2008/56246-0.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-15T12:20:57Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese SILVA AMRB.pdf: 10197611 bytes, checksum: dfa95dac75e87b0ffef8a344cb8d9996 (MD5) / Made available in DSpace on 2016-07-15T12:20:57Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese SILVA AMRB.pdf: 10197611 bytes, checksum: dfa95dac75e87b0ffef8a344cb8d9996 (MD5) Previous issue date: 2015-01-30 / CNPq / Petrobrás / A aplicação de técnicas de Análises de Componentes Principais (ACP), Redes Neurais Artificiais (RNA), Lógica Fuzzy e Sistema Neurofuzzy para investigar as alterações da característica da água das barragens de Utinga e do Bita que abastecem de água bruta a ETA Suape é de fundamental importância em função do grande número de variáveis utilizadas para definir a qualidade. Neste trabalho, foram realizadas 10 coletas de água em cada área, no período de novembro de 2007 a agosto de 2012, totalizando 120 amostras. Ainda que o conjunto de dados experimentais obtidos seja reduzido, houve múltiplos esforços em demanda da aquisição de informações da qualidade da água junto aos órgãos oficiais de monitoramento ambiental. Os resultados mostraram uma tendência à degradação da propriedade da água das barragens em decorrência da presença de microrganismos, sais e nutrientes, responsáveis pelo processo de eutrofização, o que se configurou pela maior concentração de fósforo total, Coliformes termotolerantes, e diminuição de pH e OD, provavelmente devido à ocorrência de descarte de efluentes da agroindústria canavieira, industrial e doméstico. A ACP caracterizou mais 76% das amostras permitindo visualizar a existência de mudanças sazonais e uma pequena variação espacial d`água nas barragens. A condição da água das duas barragens foi modelada satisfatoriamente, razoável precisão e confiabilidade com os modelos estatístico e computacionais, para uma quantidade de parâmetros e dados ambientais, que embora limitados foram suficientes para realização deste trabalho. Ainda assim, fica evidente a eficiência e sucesso da utilização do Sistema Neurofuzzy (coeficiente de regressão de 0,608 a 0,925) que combina as vantagens das Redes Neurais e da Lógica Fuzzy em modelar o conjunto de dados da qualidade da água das barragens de Utinga e Bita. / The application of techniques such as the Principal Components Analysis (PCAs), Artificial Neural Networks (ANNs), Fuzzy Logic and Neuro-fuzzy Systems for investigating the changes in the water quality characteristics in the Utinga and Bita dams, which supplies raw water to the Suape Wastewater Treatment Plant (WWP), is of great importance due to the high number of variables used to define water quality. In this work were collected 10 water samples used to define water quality, in a period ranging from November 2007 to August 2012, with a total of 120 samples. Although the experimental dataset was limited, there were multiple efforts in gathering information from the Environmental Control Agencies. The results showed a tendency of degradation of the water properties in the dams studied due to the presence of microorganisms, salts and nutrients, responsible for the eutrophication process; result of the higher concentration of total phosphorus, Thermotolerant Coliforms and decrease in pH and DO, probably from the discharge of the sugarcane agroindustry and domestic waste. The PCAs characterised more than 76% of the samples collected, and consequently observing the existence of seasonal changes and small spatial variation of water levels in the dams. The water quality conditions in both dams were satisfactorily modelled, obtaining a reasonable precision and statistical and computational reliability for a certain amount of parameters and environmental data that, even though considered limited, were enough to run this trial. Nonetheless, it becomes evident the efficiency and success in using the Neuro- Fuzzy System (regression coefficient of 0.608 to 0.925), which combines the advantages of both the Neural Networks and Fuzzy Logic in modelling the water quality dataset in the Utinga and Bita dams.

The main purpose of this study is to develop a predictive model capable to forecast residential real estate prices in the city of Prague using Artificial Intelligence methods. The first part of this study discusses fundamentals of Artificial Neural Networks and Fuzzy Inference Systems in the context of real estate valuation. The second part demonstrates a development and testing of such models using a dataset of real estate market transactions. In the third part, results are compared to Multiple Regression and an explanatory power of each model is evaluated. Conclusions of this research are: (1) Artificial Neural Networks and Fuzzy Inference Systems give more accurate estimates of market values of residential real estates than Multiple Regression; (2) Artificial Neural Networks and Fuzzy Inference Systems represent an efficient way of modeling and analyzing residential real estate prices in Prague.

DC to DC converters stabilize the voltage obtained from voltage sources such as solar power system, wind energy sources, wave energy sources, rectified voltage from alternators, and so forth. Hence, the need for improving its control algorithm is inevitable. Many algorithms are applied to DC to DC converters. This thesis designs fuzzy adaptive dynamic programming (Fuzzy ADP) algorithm. Also, this thesis implements both adaptive dynamic programming (ADP) and Fuzzy ADP on DC to DC converters to observe the performance of the output voltage trajectories.

Die zuverlässige Einhaltung von Lieferzusagen stellt ein wichtiges Kriterium bei der Auswahl der Teilnehmer eines auftragsbezogenen Produktionsnetzes dar. Für die objektive Bewertung der Lieferzuverlässigkeit der potenziellen Netzwerkteilnehmer bedarf es der Quantifizierung der relevanten Unsicherheiten integriert in einen allgemein gültigen Ansatz der Verfügbarkeitsprüfung. Die Arbeit stellt daraus resultierend Ansätze zur Berechnung der Unsicherheit vor. Durch die Quantifizierung der Unsicherheit innerhalb der Unternehmen ergibt sich zudem die Möglichkeit der flexiblen, situationsabhängigen Nutzung des für langfristige Rahmenverträge reservierten Sicherheitsbestandes zur Befriedigung kurzfristiger Anfragen. Diese Aufgabe unterstützt ein konfigurierbares Modell zur Entscheidungsunterstützung, das auf einem Neuro-Fuzzy-System basiert. Die Kennzahlen der Lieferzuverlässigkeit unterliegen einem dynamischen Verhalten während des Wertschöpfungsprozesses in dem auftragsbasierten Produktionsnetz. Durch die Integration dieser Kennzahlen in das Management dieses Prozesses ergibt sich die Möglichkeit, aus der Zunahme der Unsicherheit mögliche Störungen und deren Auswirkungen bereits vor ihrem Eintreten zu erfassen und im Rahmen eines präventiven Störungsmanagements zu agieren.

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.