Hysteresis Modeling in Iron-Dominated Magnets Based on a Multi-Layered Narx Neural Network Approach

A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications

Automatic Development and Adaptation of Concise Nonlinear Models for System Identification

Mathematical descriptions of natural and man-made processes are the bedrock of science, used by humans to understand, estimate, predict and control the natural and built world around them. The goal of system identification is to enable the inference of mathematical descriptions of the true behavior and dynamics of processes from their measured observations. The crux of this task is the identification of the dynamic model form (topology) in addition to its parameters. Model structures must be concise to offer insight to the user about the process in question. To that end, this dissertation proposes three methods to improve the ability of system identification to identify succinct nonlinear model structures. The first is a model structure adaptation method (MSAM) that modifies first principles models to increase their predictive ability while maintaining intelligibility. Model structure identification is achieved by this method despite the presence of parametric error through a novel means of estimating the gradient of model structure perturbations. I demonstrate MSAM\u27s ability to identify underlying nonlinear dynamic models starting from linear models in the presence of parametric uncertainty. The main contribution of this method is the ability to adapt the structure of existing models of processes such that they more closely match the process observations. The second method, known as epigenetic linear genetic programming (ELGP), conducts symbolic regression without a priori knowledge of the form of the model or its parameters. ELGP incorporates a layer of genetic regulation into genetic programming (GP) and adapts it by local search to tune the resultant model structures for accuracy and conciseness. The introduction of epigenetics is made simple by the use of a stack-based program representation. This method, tested on hundreds of dynamics problems, demonstrates the ability of epigenetic local search to improve GP by producing simpler and more accurate models. The third method relies on a multidimensional GP approach (M4GP) for solving multiclass classification problems. The proposed method uses stack-based GP to conduct nonlinear feature transformations to optimize the clustering of data according to their classes. In comparison to several state-of-the-art methods, M4GP is able to classify test data better on several real-world problems. The main contribution of M4GP is its demonstrated ability to combine the strengths of GP (e.g. nonlinear feature transformations and feature selection) with the strengths of distance-based classification. MSAM, ELGP and M4GP improve the identification of succinct nonlinear model structures for continuous dynamic processes with starting models, continuous dynamic processes without starting models, and multiclass dynamic processes without starting models, respectively. A considerable portion of this dissertation is devoted to the application of these methods to these three classes of real-world dynamic modeling problems. MSAM is applied to the restructuring of controllers to improve the closed-loop system response of nonlinear plants. ELGP is used to identify the closed-loop dynamics of an industrial scale wind turbine and to define a reduced-order model of fluid-structure interaction. Lastly, M4GP is used to identify a dynamic behavioral model of bald eagles from collected data. The methods are analyzed alongside many other state-of-the-art system identification methods in the context of model accuracy and conciseness

Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014

Dieser Tagungsband enthält die Beiträge des 24. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 27. - 28. November 2014 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Analysis of derived features for the motion classification of a passive lower limb exoskeleton

Analysis of Derived Features for the Motion Classification of a PassiveLowerLimbExoskeleton The recognition of human motion intentions is a fundamental requirement to control efficiently an exoskeleton system. The exoskeleton control can be enhanced or subsequent motions can be predicted, if the current intended motion is known. At H2T research has been carried out with a classification system based on Hidden Markov Models (HMMs) to classify the multi-modal sensor data acquired from a unilateral passive lower-limb exoskeleton. The training data is formed of force vectors, linear accelerations and Euler angles provided by 7 3D-force sensors and 3 IMUs. The recordings consist of data of 10 subjects performing 14 different types of daily activities, each one carried out 10 times. This master thesis attempts to improve the motion classification by using physical meaningful derived features from the raw data aforementioned. The knee vector moment and the knee and ankle joint angles, which respectively give a kinematic and dynamic description of a motion, were the derived features considered. Firstly, these new features are analysed to study their patterns and the resemblance of the data among different subjects is quantified in order to check their consistency. Afterwards, the derived features are evaluated in the motion classification system to check their performance. Various configurations of the classifier were tested including different preprocessors of the data employed and the structure of the HMMs used to represent each motion. Some setups combining derived features and raw data led to good results (e.g. norm of the moment vector and IMUs got 89.39% of accuracy), but did not improve the best results of previous works (e.g. 2 IMUs and 1 Force Sensor got 90.73% of accuracy). Although the classification results are not improved, it is proved that these derived features are a good representation of their primary features and a suitable option if a dimensional reduction of the data is pursued. At the end, possible directions of improvement are suggested to improve the motion classification concerning the results obtained along the thesis.Outgoin