İleri beslemeli ve elman geri beslemeli yapay sinir ağları kullanarak harmoniklerin kompanzasyonu

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Destek Vektör Makineleri, Aşamalı Öğrenme, Destek Vektör Sınırlayıcılar, Tam Unutma Problemi, Sınırlayıcılar Topluluğu, Oy Dışı Bırakma, Örüntü Tanıma, Örüntü Sınıflama, Elektronik Burun. Destek Vektör Makineleri (DVM), ileri yönde beslemeli Yapay Sinir Ağlarının (YSA) yeni bir türüdür. DVM, istatistiksel öğrenme teorisinde iyi şekilde kurgulanmış bir teoriye sahiptir ve çok sayıda sınıflandırma ve regresyon problemlerinin çözümü için başarılı bir şekilde uygulanmıştır. DVM öğrenme boyunca, iki sınıflı bir sınıflandırma probleminde çoklu karar-düzleminin her iki tarafında yer alan veri örnekleri arasındaki mesafenin maksimum olması için çoklu-karar düzleminin pozisyonunu optimize eder. Global öğrenme tekniğini kullandıkları için tam unutma probleminden etkilenirler. Tam unutma (öğrenememe), önceden öğrendiklerini unutmadan yeni veri ile sunulan bilgiyi öğrenmek için sistemin yetersiz kalışıdır. Bu problemin üstesinden gelen yöntemler, problem uzayından üretilen yeni veri kümeleri üzerinde veya geçmişteki verinin seçiminde YSA'nm prova ya da tekrarlar ile eğitilmesini içermektedir. Learn"1-1" yeni sunulan aşamalı bir öğrenme algoritmasıdır. Learn"1-1" algoritmasının gücü, önceden görülen veriye herhangi bir erişime gerek kalmadan ve önceden elde edilen bilgiyi unutmadan yeni veriyi öğrenebilmesinde yatmaktadır. Learn^.MT algoritması ise Learn^'in etkilendiği oy dışı bırakma probleminin etkisini çözmek için geliştirilmiştir. Bu çalışmada, DVM'in tam unutma problemine işaret etmek ve bu problemi çözerek onlara aşamalı öğrenme yeteneği kazandırmak amacıyla, DVM sınırlayıcısının Learn"" ve Learn^JVıT algoritmaları ile entegrasyonu önerilmiştir. "Önerilen yaklaşımlar, DVMLearn"" ve DVMLearn^.MT, ile gerçek dünya verileri ve karşılaştırma amaçlı hazırlanmış olan veriler üzerinde yapılan deneylerden, yüksek bir genelleştirme performansıyla beraber başarılı ve ümit verici sonuçlar elde edilmiştir. Bunlara ilave olarak, çalışmamızda DVM sınırlayıcıların aşamalı öğrenme yeteneğini test etmek amacıyla elektronik burun (e-Burun) verileri üzerinde biraz daha çok yoğunlaşılmıştır. DVM, son zamanlarda yapılan bazı çalışmalarda e-Burun verilerini XIIIsınıflandırılma probleminin çözümüne uygulanmıştır. DVM, e-Burun verilerinin tespit ve sınıflandırmasında iyi bir genelleştirme performansı ortaya koymaktadırlar. Buna rağmen, e-Burun verilerini içeren pek çok uygulamada, zamana göre - hatta yeni sınıflan içerebilen - ek veri elde edilmesi karşılaşılan bir durumdur. e-Burun verisinin doğasından dolayı, önceden kazanılan bilgiyi kaybetmeden aşamalı bir şekilde sınıflandırma yeteneklerini test etmek için DVMLearn44^ ve DVMLearn++.MT ile deneyler yapılmıştır. Her iki yaklaşımdan elde edilen sonuçlar kendi aralarında ve benzer bir şekilde taban sınıflayıcı olarak Radyal Tabanlı Ağ (RTA) fonksiyon sinir ağının aşamalı bir şekilde kurgulanmasından elde edilen sonuçlarla karşılaştırılmıştır.INCREMENTAL LEARNING WITH SUPPORT VECTOR MACHINES CLASSIFIER SUMMARY Key words: Support Vector Machines, Incremental Learning, Support Vector Classifier, Catastrophic Forgetting Problem, Ensemble of Classifier, Out-voting, Pattern Classification, Electronic Nose. Support Vector Machines (SVMs) are a new category of universal feedforward neural networks. SVMs have been well founded in the framework of the statistical learning theory, and successfully applied to solve a large number of classification and regression problems. Through learning, the SVM optimizes the positioning of the decision hyperplanes to achieve maximum distance from all data samples on both sides of the hyperplane on a two class problem. Since SVMs employ a global learning technique, they suffer from the catastrophic forgetting phenomenon. Catastrophic forgetting (also called unlearning) is the inability of the system to learn new patterns without forgetting those that were previously learned. Methods to deal with this problem include rehearsing the neural networks on a selection of past data, or on new data points generated from the problem space. Learn++ have recently been introduced as an incremental learning algorithm. The strength of Learn"1-1" lies in its ability to learn new data without forgetting previously acquired knowledge and without requiring access to any of the previously seen data, even when the new data introduce new classes. The Learn++.MT algorithm is proposed to solve the effect of out-voting problem of Learn"1-1". To address the catastrophic forgetting problem and to add the incremental learning capability to SVMs, training an ensemble of SVMs using Learn1-1" and Learn^.MT is proposed in this work. Experiments with the real-world and benchmark datasets show that the proposed approaches, DVMLearn"1-1" and DVMLearn^.MT, introduce successful and promising results with high generalization performance. In addition, SVMs have been applied to solve the classification of electronic nose (E- nose) data in some recent studies. They provide good generalization performance in detection and classification of E-nose data. However, in many applications involving xvE-nose data, it is not unusual for additional data - which may include new classes - to become available over time, which then requires a classifier that is capable of incremental learning that does not suffer from loss of previously acquired knowledge. In our contribution, the ability of SVMLearn"1-1" and SVMLearn++.MT to incrementally classify E-nose data are evaluated and compared to each other and against a similarly constructed Learn*"1" algorithm that uses radial basis function neural network as base classifiers. XV

Compliant control of Uni/ Multi- robotic arms with dynamical systems

Accomplishment of many interactive tasks hinges on the compliance of humans. Humans demonstrate an impressive capability of complying their behavior and more particularly their motions with the environment in everyday life. In humans, compliance emerges from different facets. For example, many daily activities involve reaching for grabbing tasks, where compliance appears in a form of coordination. Humans comply their handsâ motions with each other and with that of the object not only to establish a stable contact and to control the impact force but also to overcome sensorimotor imprecisions. Even though compliance has been studied from different aspects in humans, it is primarily related to impedance control in robotics. In this thesis, we leverage the properties of autonomous dynamical systems (DS) for immediate re-planning and introduce active complaint motion generators for controlling robots in three different scenarios, where compliance does not necessarily mean impedance and hence it is not directly related to control in the force/velocity domain. In the first part of the thesis, we propose an active compliant strategy for catching objects in flight, which is less sensitive to the timely control of the interception. The soft catching strategy consists in having the robot following the object for a short period of time. This leaves more time for the fingers to close on the object at the interception and offers more robustness than a âhardâ catching method in which the hand waits for the object at the chosen interception point. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction. Stability and convergence of the approach are assessed through Lyapunov stability theory. In the second part, we propose a unified compliant control architecture for coordinately reaching for grabbing a moving object by a multi-arm robotic system. Due to the complexity of the task and of the system, each arm complies not only with the objectâs motion but also with the motion of other arms, in both task and joint spaces. At the task-space level, we propose a unified dynamical system that endows the multi-arm system with both synchronous and asynchronous behaviors and with the capability of smoothly transitioning between the two modes. At the joint space level, the compliance between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under self-collision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in real-time. In the last part, we propose a compliant dynamical system for stably transitioning from free motions to contacts. In this part, by modulating the robot's velocity in three regions, we show theoretically and empirically that the robot can (I) stably touch the contact surface (II) at a desired location, and (III) leave the surface or stop on the surface at a desired point

A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning

Despite tremendous advances in robotics, we are still amazed by the proficiency with which humans perform movements. Even new waves of robotic systems still rely heavily on hardcoded motions with a limited ability to react autonomously and robustly to a dynamically changing environment. This thesis focuses on providing possible mechanisms to push the level of adaptivity, reactivity, and robustness of robotic systems closer to human movements. Specifically, it aims at developing these mechanisms for a subclass of robot motions called “reaching movements”, i.e. movements in space stopping at a given target (also referred to as episodic motions, discrete motions, or point-to-point motions). These reaching movements can then be used as building blocks to form more advanced robot tasks. To achieve a high level of proficiency as described above, this thesis particularly seeks to derive control policies that: 1) resemble human motions, 2) guarantee the accomplishment of the task (if the target is reachable), and 3) can instantly adapt to changes in dynamic environments. To avoid manually hardcoding robot motions, this thesis exploits the power of machine learning techniques and takes an Imitation Learning (IL) approach to build a generic model of robot movements from a few examples provided by an expert. To achieve the required level of robustness and reactivity, the perspective adopted in this thesis is that a reaching movement can be described with a nonlinear Dynamical System (DS). When building an estimate of DS from demonstrations, there are two key problems that need to be addressed: the problem of generating motions that resemble at best the demonstrations (the “how-to-imitate” problem), and most importantly, the problem of ensuring the accomplishment of the task, i.e. reaching the target (the “stability” problem). Although there are numerous well-established approaches in robotics that could answer each of these problems separately, tackling both problems simultaneously is challenging and has not been extensively studied yet. This thesis first tackles the problem mentioned above by introducing an iterative method to build an estimate of autonomous nonlinear DS that are formulated as a mixture of Gaussian functions. This method minimizes the number of Gaussian functions required for achieving both local asymptotic stability at the target and accuracy in following demonstrations. We then extend this formulation and provide sufficient conditions to ensure global asymptotic stability of autonomous DS at the target. In this approach, an estimation of the underlying DS is built by solving a constraint optimization problem, where the metric of accuracy and the stability conditions are formulated as the optimization objective and constraints, respectively. In addition to ensuring convergence of all motions to the target within the local or global stability regions, these approaches offer an inherent adaptability and robustness to changes in dynamic environments. This thesis further extends the previous approaches and ensures global asymptotic stability of DS-based motions at the target independently of the choice of the regression technique. Therefore, it offers the possibility to choose the most appropriate regression technique based on the requirements of the task at hand without compromising DS stability. This approach also provides the possibility of online learning and using a combination of two or more regression methods to model more advanced robot tasks, and can be applied to estimate motions that are represented with both autonomous and non-autonomous DS. Additionally, this thesis suggests a reformulation to modeling robot motions that allows encoding of a considerably wider set of tasks ranging from reaching movements to agile robot movements that require hitting a given target with a specific speed and direction. This approach is validated in the context of playing the challenging task of minigolf. Finally, the last part of this thesis proposes a DS-based approach to realtime obstacle avoidance. The presented approach provides a modulation that instantly modifies the robot’s motion to avoid collision with multiple static and moving convex obstacles. This approach can be applied on all the techniques described above without affecting their adaptability, swiftness, or robustness. The techniques that are developed in this thesis have been validated in simulation and on different robotic platforms including the humanoid robots HOAP-3 and iCub, and the robot arms KATANA, WAM, and LWR. Throughout this thesis we show that the DS-based approach to modeling robot discrete movements can offer a high level of adaptability, reactivity, and robustness almost effortlessly when interacting with dynamic environments