We consider supervised learning problems, where one is given objects with labels, and the goal is to learn a model that can make accurate predictions on new objects. These problems abound in applications, ranging from medical diagnosis to information retrieval to computer vision. Examples include binary or multiclass classi cation, where the goal is to learn a model that can classify objects into two or more categories (e.g. categorizing emails into spam or non-spam); bipartite ranking, where the goal is to learn a model that can rank relevant objects above the irrelevant ones (e.g. ranking documents by relevance to a query); class probability estimation (CPE), where the goal is to predict the probability of an object belonging to different categories (e.g. probability of an internet ad being clicked by a user). In each case, the accuracy of a model is evaluated in terms of a specified `performance measure'. While there has been much work on designing and analyzing algorithms for different supervised learning tasks, we have complete understanding only for settings where the performance measure of interest is the standard 0-1 or a loss-based classification measure. These performance measures have a simple additive structure, and can be expressed as an expectation of errors on individual examples. However, in many real-world applications, the performance measure used to evaluate a model is often more complex, and does not decompose into a sum or expectation of point-wise errors. These include the binary or multiclass G-mean used in class-imbalanced classification problems; the F1-measure and its multiclass variants popular in text retrieval; and the (partial) area under the ROC curve (AUC) and precision@ employed in ranking applications. How does one design efficient learning algorithms for such complex performance measures, and can these algorithms be shown to be statistically consistent, i.e. shown to converge in the limit of infinite data to the optimal model for the given measure? How does one develop efficient learning algorithms for complex measures in online/streaming settings where the training examples need to be processed one at a time? These are questions that we seek to address in this thesis. Firstly, we consider the bipartite ranking problem with the AUC and partial AUC performance measures. We start by understanding how bipartite ranking with AUC is related to the standard 0-1 binary classification and CPE tasks. It is known that a good binary CPE model can be used to obtain both a good binary classification model and a good bipartite ranking model (formally, in terms of regret transfer bounds), and that a binary classification model does not necessarily yield a CPE model. However, not much is known about other directions. We show that in a weaker sense (where the mapping needed to transform a model from one problem to another depends on the underlying probability distribution), a good bipartite ranking model for AUC can indeed be used to construct a good binary classification model, and also a good binary CPE model. Next, motivated by the increasing number of applications (e.g. biometrics, medical diagnosis, etc.), where performance is measured, not in terms of the full AUC, but in terms of the partial AUC between two false positive rates (FPRs), we design batch algorithms for optimizing partial AUC in any given FPR range. Our algorithms optimize structural support vector machine based surrogates, which unlike for the full AUC; do not admit a straightforward decomposition into simpler terms. We develop polynomial time cutting plane solvers for solving the optimization, and provide experiments to demonstrate the efficacy of our methods. We also present an application of our approach to predicting chemotherapy outcomes for cancer patients, with the aim of improving treatment decisions. Secondly, we develop algorithms for optimizing (surrogates for) complex performance mea-sures in the presence of streaming data. A well-known method for solving this problem for standard point-wise surrogates such as the hinge surrogate, is the stochastic gradient descent (SGD) method, which performs point-wise updates using unbiased gradient estimates. How-ever, this method cannot be applied to complex objectives, as here one can no longer obtain unbiased gradient estimates from a single point. We develop a general stochastic method for optimizing complex measures that avoids point-wise updates, and instead performs gradient updates on mini-batches of incoming points. The method is shown to provably converge for any performance measure that satis es a uniform convergence requirement, such as the partial AUC, precision@ and F1-measure, and in experiments, is often several orders of magnitude faster than the state-of-the-art batch methods, while achieving similar or better accuracies. Moreover, for specific complex binary classification measures, which are concave functions of the true positive rate (TPR) and true negative rate (TNR), we are able to develop stochastic (primal-dual) methods that can indeed be implemented with point-wise updates, by using an adaptive linearization scheme. These methods admit convergence rates that match the rate of the SGD method, and are again several times faster than the state-of-the-art methods. Finally, we look at the design of consistent algorithms for complex binary and multiclass measures. For binary measures, we consider the practically popular plug-in algorithm that constructs a classifier by applying an empirical threshold to a suitable class probability estimate, and provide a general methodology for proving consistency of these methods. We apply this technique to show consistency for the F1-measure, and under a continuity assumption on the distribution, for any performance measure that is monotonic in the TPR and TNR. For the case of multiclass measures, a simple plug-in method is no longer tractable, as in the place of a single threshold parameter, one needs to tune at least as many parameters as the number of classes. Using an optimization viewpoint, we provide a framework for designing learning algorithms for multiclass measures that are general functions of the confusion matrix, and as an instantiation, provide an e cient and provably consistent algorithm based on the bisection method for multiclass measures that are ratio-of-linear functions of the confusion matrix (e.g. micro F1). The algorithm outperforms the state-of-the-art SVMPerf method in terms of both accuracy and running time. Overall, in this thesis, we have looked at various aspects of complex performance measures used in supervised learning problems, leading to several new algorithms that are often significantly better than the state-of-the-art, to improved theoretical understanding of the performance measures studied, and to novel real-life applications of the algorithms developed.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / The increasing demand for knowledge representation and exchange on the semantic web has resulted in an increase in both the number and size of ontologies. This increased features in ontologies has made them more complex and in turn difficult to select, reuse and maintain them. Several ontology evaluations and ranking tools have been proposed recently. Such evaluation tools provide a metrics suite that evaluates the content of an ontology by analysing their schemas and instances. The presence of ontology metric suites may enable classification techniques in placing the ontologies in various categories or classes. Machine Learning algorithms mostly based on statistical methods used in classification of data makes them the perfect tools to be used in performing classification of ontologies. In this study, popular Machine Learning algorithms including K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Naïve Bayes, Linear Regression and Logistic Regression were used in the classification of ontologies based on their complexity metrics. A total of 200 biomedical ontologies were downloaded from the Bio Portal repository. Ontology metrics were then generated using the OntoMetrics tool, an online ontology evaluation platform. These metrics constituted the dataset used in the implementation of the machine learning algorithms. The results obtained were evaluated with performance evaluation techniques, namely, precision, recall, F-Measure Score and Receiver Operating Characteristic (ROC) curves. The Overall accuracy scores for K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Naïve Bayes, Logistic Regression and Linear Regression algorithms were 66.67%, 65%, 98%, 99.29%, 74%, 64.67%, and 57%, respectively. From these scores, Decision Trees and Random Forests algorithms were the best performing and can be attributed to the ability to handle multiclass classifications.

The Swedish real estate market has been digitalized over the past decade with the current practice being to post your real estate advertisement online. A question that has arisen is how a seller can optimize their public listing to maximize the selling premium. This paper analyzes the use of three machine learning methods to solve this problem: Linear Regression, Decision Tree Regressor and Random Forest Regressor. The aim is to retrieve information regarding how certain attributes contribute to the premium value. The dataset used contains apartments sold within the years of 2014-2018 in the Östermalm / Djurgården district in Stockholm, Sweden. The resulting models returned an R2-value of approx. 0.26 and Mean Absolute Error of approx. 0.06. While the models were not accurate regarding prediction of premium, information was still able to be extracted from the models. In conclusion, a high amount of views and a publication made in April provide the best conditions for an advertisement to reach a high selling premium. The seller should try to keep the amount of days since publication lower than 15.5 days and avoid publishing on a Tuesday. / Den svenska bostadsmarknaden har blivit alltmer digitaliserad under det senaste årtiondet med nuvarande praxis att säljaren publicerar sin bostadsannons online. En fråga som uppstår är hur en säljare kan optimera sin annons för att maximera budpremie. Denna studie analyserar tre maskininlärningsmetoder för att lösa detta problem: Linear Regression, Decision Tree Regressor och Random Forest Regressor. Syftet är att utvinna information om de signifikanta attribut som påverkar budpremien. Det dataset som använts innehåller lägenheter som såldes under åren 2014-2018 i Stockholmsområdet Östermalm / Djurgården. Modellerna som togs fram uppnådde ett R²-värde på approximativt 0.26 och Mean Absolute Error på approximativt 0.06. Signifikant information kunde extraheras from modellerna trots att de inte var exakta i att förutspå budpremien. Sammanfattningsvis skapar ett stort antal visningar och en publicering i april de bästa förutsättningarna för att uppnå en hög budpremie. Säljaren ska försöka hålla antal dagar sedan publicering under 15.5 dagar och undvika att publicera på tisdagar.

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmen-tal disorder that affects social communication, behavior, and cognitive development.Patients with autism have a variety of difficulties, such as sensory impairments, at-tention issues, learning disabilities, mental health issues like anxiety and depression,as well as motor and learning issues. The World Health Organization (WHO) es-timates that one in 100 children have ASD. Although ASD cannot be completelytreated, early identification of its symptoms might lessen its impact. Early identifi-cation of ASD can significantly improve the outcome of interventions and therapies.So, it is important to identify the disorder early. Machine learning algorithms canhelp in predicting ASD. In this thesis, Support Vector Machine (SVM) and RandomForest (RF) are the algorithms used to predict ASD. Objectives: The main objective of this thesis is to build and train the models usingmachine learning(ML) algorithms with the default parameters and with the hyper-parameter tuning and find out the most accurate model based on the comparison oftwo experiments to predict whether a person is suffering from ASD or not. Methods: Experimentation is the method chosen to answer the research questions.Experimentation helped in finding out the most accurate model to predict ASD. Ex-perimentation is followed by data preparation with splitting of data and by applyingfeature selection to the dataset. After the experimentation followed by two exper-iments, the models were trained to find the performance metrics with the defaultparameters, and the models were trained to find the performance with the hyper-parameter tuning. Based on the comparison, the most accurate model was appliedto predict ASD. Results: In this thesis, we have chosen two algorithms SVM and RF algorithms totrain the models. Upon experimentation and training of the models using algorithmswith hyperparameter tuning. SVM obtained the highest accuracy score and f1 scoresfor test data are 96% and 97% compared to other model RF which helps in predictingASD. Conclusions: The models were trained using two ML algorithms SVM and RF andconducted two experiments, in experiment-1 the models were trained using defaultparameters and obtained accuracy, f1 scores for the test data, and in experiment-2the models were trained using hyper-parameter tuning and obtained the performancemetrics such as accuracy and f1 score for the test data. By comparing the perfor-mance metrics, we came to the conclusion that SVM is the most accurate algorithmfor predicting ASD.

Serienfertiger im Bereich der Innenhochdruck-Umformung stehen unter starkem Wettbewerbsdruck alternativer klassischer Fertigungen und deren Kostenkriterien. Wechselnde Produktionsanforderungen im globalisierten Marktumfeld erfordern flexibles Handeln bei höchster Qualität und niedrigen Kosten. Durch Reduzierung der Lager- und Umlaufbestände können Kosteneinsparungen erzielt werden. Störungsbedingte Ausfälle an IHU-Anlagen gilt es dabei auf ein Minimum zu reduzieren, um die vereinbarten Liefertermine fristgerecht zu erfüllen und Konventionalstrafen zu umgehen. Die erforderliche Produktivität und das angestrebte Qualitätsniveau lässt sich nur durch angepasste Instandhaltungsstrategien aufrechterhalten, weshalb ein Konzept für die vorausschauende Instandhaltung mit integriertem Qualitätsmanagement speziell für den Bereich der IHU erarbeitet wurde. Dynamische Prozess- und Instandhaltungsanpassungen sind zentraler Bestandteil der Entwicklungsarbeit.

Series manufacturers in the field of hydroforming face intense competition from alternative conventional manufacturing methods and their cost criteria. Changing production requirements in the globalized market environment require flexible action with highest quality and low costs. Cost savings can be achieved through reductions in warehouse and circulating stocks. Malfunction-related downtimes in hydroforming systems must be reduced to a minimum in order to meet the agreed delivery dates on time and avoid conventional penalties. The required productivity and the desired quality level can only be maintained through adapted maintenance strategies, leading to the development of a concept for predictive maintenance integrated with quality management specifically for the IHU domain. Dynamic process and maintenance adaptations are a central component to this developmental effort.

Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................88

[pt] Nesta tese buscamos fornecer duas diferentes abordagens para a otimização de carteiras de ativos sob incerteza. Demonstramos como a incerteza acerca da distribuição dos retornos esperados pode ser incorporada nas decisões de alocação de ativos, utilizando as seguintes ferramentas: (1) uma extensão da metodologia Bayesiana proposta por Black e Litterman através de uma estratégia de negociação dinâmica construída sobre um modelo de aprendizagem com base na análise fundamentalista, (2 ) uma abordagem adaptativa baseada em técnicas de otimização robusta. Esta última abordagem é apresentada em duas diferentes especificações: uma modelagem robusta com base em uma análise puramente empírica e uma extensão da modelagem robusta proposta por Bertsimas e Sim em 2004. Para avaliar a importância dos modelos propostos no tratamento da incerteza na distribuição dos retornos examinamos a extensão das mudanças nas carteiras ótimas geradas. As principais conclusões são: (a ) é possível obter carteiras ótimas menos influenciadas por erros de estimação, ( b ) tais carteiras são capazes de gerar retornos estatisticamente superiores com perdas bem controladas, quando comparadas com carteiras ótimas de Markowitz e índices de referência selecionados. / [en] In this thesis we provide two different approaches for determining optimal asset allocation portfolios under uncertainty. We show how uncertainty about expected returns distribution can be incorporated in asset allocation decisions by using the following alternative frameworks: (1) an extension of the Bayesian methodology proposed by Black and Litterman through a dynamic trading strategy built on a learning model based on fundamental analysis; (2) an adaptive dynamic approach, based on robust optimization techniques. This latter approach is presented in two different specifications: an empirical robust loss model and a covariancebased robust loss model based on Bertsimas and Sim approach to model uncertainty sets. To evaluate the importance of the proposed models for distribution uncertainty, the extent of changes in the prior optimal asset allocations of investors who embody uncertainty in their portfolio is examined. The key findings are: (a) it is possible to achieve optimal portfolios less influenced by estimation errors; (b) portfolio strategies of such investors generate statistically higher returns with controlled losses when compared to the classical mean-variance optimized portfolios and selected benchmarks.

With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies. / Doctor of Philosophy / The modern power grid is transforming from unidirectional to transactive power systems. Distributed peer-to-peer (P2P) energy trading is becoming more and more popular. Rooftop PV energy and negawatt-hours as two main sources of electricity assets are playing important roles in peer-to-peer energy trading. It enables the building owner to join the electricity market as both energy consumer and producer, also named prosumer. While P2P energy trading participants are usually un-informed and do not know how much energy they can generate during the next hour. Thus, a system is needed to guide the participant to submit a reasonable amount of PV energy or negawatt-hours to be supplied. This dissertation develops a machine learning-based anomaly detection model for an energy trading platform to detect the reasonable PV energy and negawatt-hours available for the next hour's electricity trading market. The anomaly detection performance of this framework is analyzed. The building load forecasting model used in negawatt-hour trading also considers the effect of building occupancy level and HVAC setpoint adjustment. Moreover, the implication of CO2 measurement devices to monitor building occupancy levels is demonstrated. Finally, a simple Hyperledger-based electricity trading platform that enables participants to sell photovoltaic solar energy/ negawatt-hours to other participants is simulated to demonstrate the potential benefits of blockchain.

Αντικείµενο της διατριβής είναι η ανάπτυξη νέων µεθοδολογιών εκµάθησης και σύγκλισης των Ασαφών Γνωστικών ∆ικτύων που προτείνονται για τη βελτίωση και προσαρµογή της συµπεριφοράς τους, καθώς και για την αύξηση της απόδοσής τους, αναδεικνύοντάς τα σε αποτελεσµατικά δυναµικά συστήµατα µοντελοποίησης. Τα νέα βελτιωµένα Ασαφή Γνωστικά ∆ίκτυα, µέσω της εκµάθησης και προσαρµογής των βαρών τους, έχουν χρησιµοποιηθεί στην ιατρική σε θέµατα διάγνωσης και υποστήριξης στη λήψη απόφασης, καθώς και σε µοντέλα βιοµηχανικών συστηµάτων που αφορούν τον έλεγχο διαδικασιών, µε πολύ ικανοποιητικά αποτελέσµατα. Στη διατριβή αυτή παρουσιάζονται, αξιολογούνται και εφαρµόζονται δύο νέοι αλγόριθµοι εκµάθησης χωρίς επίβλεψη των Ασαφών Γνωστικών ∆ικτύων, οι αλγόριθµοι Active Hebbian Learning (AHL) και Nonlinear Hebbian Learning (NHL), βασισµένοι στον κλασσικό αλγόριθµό εκµάθησης χωρίς επίβλεψη τύπου Hebb των νευρωνικών δικτύων, καθώς και µια νέα προσέγγιση εκµάθησης των Ασαφών Γνωστικών ∆ικτύων βασισµένη στους εξελικτικούς αλγορίθµους και πιο συγκεκριµένα στον αλγόριθµο Βελτιστοποίησης µε Σµήνος Σωµατιδίων και στον ∆ιαφοροεξελικτικό αλγόριθµο. Οι προτεινόµενοι αλγόριθµοι AHL και NHL στηρίζουν νέες µεθοδολογίες εκµάθησης για τα ΑΓ∆ που βελτιώνουν τη λειτουργία, και την αξιοπιστία τους, και που παρέχουν στους εµπειρογνώµονες του εκάστοτε προβλήµατος που αναπτύσσουν το ΑΓ∆, την εκµάθηση των παραµέτρων για τη ρύθµιση των αιτιατών διασυνδέσεων µεταξύ των κόµβων. Αυτοί οι τύποι εκµάθησης που συνοδεύονται από την σωστή γνώση του εκάστοτε προβλήµατος-συστήµατος, συµβάλλουν στην αύξηση της απόδοσης των ΑΓ∆ και διευρύνουν τη χρήση τους. Επιπρόσθετα µε τους αλγορίθµους εκµάθησης χωρίς επίβλεψη τύπου Hebb για τα ΑΓ∆, αναπτύσσονται και προτείνονται νέες τεχνικές εκµάθησης των ΑΓ∆ βασισµένες στους εξελικτικούς αλγορίθµους. Πιο συγκεκριµένα, προτείνεται µια νέα µεθοδολογία για την εφαρµογή του εξελικτικού αλγορίθµου Βελτιστοποίησης µε Σµήνος Σωµατιδίων στην εκµάθηση των Ασαφών Γνωστικών ∆ικτύων και πιο συγκεκριµένα στον καθορισµό των βέλτιστων περιοχών τιµών των βαρών των Ασαφών Γνωστικών ∆ικτύων. Με τη µεθοδο αυτή λαµβάνεται υπόψη η γνώση των εµπειρογνωµόνων για τον σχεδιασµό του µοντέλου µε τη µορφή περιορισµών στους κόµβους που µας ενδιαφέρουν οι τιµές των καταστάσεών τους, που έχουν οριστοί ως κόµβοι έξοδοι του συστήµατος, και για τα βάρη λαµβάνονται υπόψη οι περιοχές των ασαφών συνόλων που έχουν συµφωνήσει όλοι οι εµπειρογνώµονες. Έτσι θέτoντας περιορισµούς σε όλα τα βάρη και στους κόµβους εξόδου και καθορίζοντας µια κατάλληλη αντικειµενική συνάρτηση για το εκάστοτε πρόβληµα, προκύπτουν κατάλληλοι πίνακες βαρών (appropriate weight matrices) που µπορούν να οδηγήσουν το σύστηµα σε επιθυµητές περιοχές λειτουργίας και ταυτόχρονα να ικανοποιούν τις ειδικές συνθήκες- περιορισµούς του προβλήµατος. Οι δύο νέες µέθοδοι εκµάθησης χωρίς επίβλεψη που έχουν προταθεί για τα ΑΓ∆ χρησιµοποιούνται και εφαρµόζονται µε επιτυχία σε δυο πολύπλοκα προβλήµατα από το χώρο της ιατρικής, στο πρόβληµα λήψης απόφασης στην ακτινοθεραπεία και στο πρόβληµα κατηγοριοποίησης των καρκινικών όγκων της ουροδόχου κύστης σε πραγµατικές κλινικές περιπτώσεις. Επίσης όλοι οι προτεινόµενοι αλγόριθµοι εφαρµόζονται σε µοντέλα βιοµηχανικών συστηµάτων που αφορούν τον έλεγχο διαδικασιών µε πολύ ικανοποιητικά αποτελέσµατα. Οι αλγόριθµοι αυτοί, όπως προκύπτει από την εφαρµογή τους σε συγκεκριµένα προβλήµατα, βελτιώνουν το µοντέλο του ΑΓ∆, συµβάλλουν σε ευφυέστερα συστήµατα και διευρύνουν τη δυνατότητα εφαρµογής τους σε πραγµατικά και πολύπλοκα προβλήµατα. Η κύρια συνεισφορά αυτής της διατριβής είναι η ανάπτυξη νέων µεθοδολογιών εκµάθησης και σύγκλισης των Ασαφών Γνωστικών ∆ικτύων προτείνοντας δυο νέους αλγορίθµους µη επιβλεπόµενης µάθησης τύπου Hebb, τον αλγόριθµο Active Hebbian Learning και τον αλγόριθµο Nonlinear Hebbian Learning για την προσαρµογή των βαρών των διασυνδέσεων µεταξύ των κόµβων των Ασαφών Γνωστικών ∆ικτύων, καθώς και εξελικτικούς αλγορίθµους βελτιστοποιώντας συγκεκριµένες αντικειµενικές συναρτήσεις για κάθε εξεταζόµενο πρόβληµα. Τα νέα βελτιωµένα Ασαφή Γνωστικά ∆ίκτυα µέσω των αλγορίθµων προσαρµογής των βαρών τους έχουν χρησιµοποιηθεί για την ανάπτυξη ενός ∆ιεπίπεδου Ιεραρχικού Συστήµατος για την υποστήριξη λήψης απόφασης στην ακτινοθεραπεία, για την ανάπτυξη ενός διαγνωστικού εργαλείου για την κατηγοριοποίηση του βαθµού κακοήθειας των καρκινικών όγκων της ουροδόχου κύστης, καθώς και για την επίλυση βιοµηχανικών προβληµάτων για τον έλεγχο διαδικασιών. / The main contribution of this Dissertation is the development of new learning and convergence methodologies for Fuzzy Cognitive Maps that are proposed for the improvement and adaptation of their behaviour, as well as for the increase of their performance, electing them in effective dynamic systems of modelling. The new improved Fuzzy Cognitive Maps, via the learning and adaptation of their weights, have been used in medicine for diagnosis and decision-making, as well as to alleviate the problem of the potential uncontrollable convergence to undesired states in models of industrial process control systems, with very satisfactory results. In this Dissertation are presented, validated and implemented two new learning algorithms without supervision for Fuzzy Cognitive Maps, the algorithms Active Hebbian Learning (AHL) and Nonlinear Hebbian Learning (NHL), based on the classic unsupervised Hebb-type learning algorithm of neural networks, as well as a new approach of learning for Fuzzy Cognitive Maps based on the evolutionary algorithms and more specifically on the algorithm of Particles Swarm Optimization and on the Differential Evolution algorithm. The proposed algorithms AHL and NHL support new learning methodologies for FCMs that improve their operation, efficiency and reliability, and that provide in the experts of each problem that develop the FCM, the learning of parameters for the regulation (fine-tuning) of cause-effect relationships (weights) between the concepts. These types of learning that are accompanied with the right knowledge of each problem-system, contribute in the increase of performance of FCMs and extend their use. Additionally to the unsupervised learning algorithms of Hebb-type for the FCMs, are developed and proposed new learning techniques of FCMs based on the evolutionary algorithms. More specifically, it is proposed a new learning methodology for the application of evolutionary algorithm of Particle Swarm Optimisation in the adaptation of FCMs and more concretely in the determination of the optimal regions of weight values of FCMs. With this method it is taken into consideration the experts’ knowledge for the modelling with the form of restrictions in the concepts that interest us their values, and are defined as output concepts, and for weights are received the arithmetic values of the fuzzy regions that have agreed all the experts. Thus considering restrictions in all weights and in the output concepts and determining a suitable objective function for each problem, result appropriate weight matrices that can lead the system to desirable regions of operation and simultaneously satisfy specific conditions of problem. The first two proposed methods of unsupervised learning that have been suggested for the FCMs are used and applied with success in two complicated problems in medicine, in the problem of decision-making in the radiotherapy process and in the problem of tumor characterization for urinary bladder in real clinical cases. Also all the proposed algorithms are applied in models of industrial systems that concern the control of processes with very satisfactory results. These algorithms, as it results from their application in concrete problems, improve the model of FCMs, they contribute in more intelligent systems and they extend their possibility of application in real and complex problems. The main contribution of the present Dissertation is to develop new learning and convergence methodologies for Fuzzy Cognitive Maps proposing two new unsupervised learning algorithms, the algorithm Active Hebbian Learning and the algorithm Nonlinear Hebbian Learning for the adaptation of weights of the interconnections between the concepts of Fuzzy Cognitive Maps, as well as Evolutionary Algorithms optimizing concrete objective functions for each examined problem. New improved Fuzzy Cognitive Maps via the algorithms of weight adaptation have been used for the development of an Integrated Two-level hierarchical System for the support of decision-making in the radiotherapy, for the development of a new diagnostic tool for tumour characterization of urinary bladder, as well as for the solution of industrial process control problems.