6,236 research outputs found
Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?
Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring
and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back
-prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCIâs EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for
comprehensible classification without scarifying accuracy.
GMDH is suggested to be used to optimally classify a given
set of BCIâs EEG signals. The other areas related to BCI will
also be addressed yet within the context of this purpose
The application of neural networks to the SSME startup transient
Feedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter, the high pressure oxidizer turbine discharge temperature, a redlined parameter, and the high pressure fuel pump discharge pressure, a failure-indicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set
Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility
Trial-varying disturbances are a key concern in Iterative Learning Control
(ILC) and may lead to inefficient and expensive implementations and severe
performance deterioration. The aim of this paper is to develop a general
framework for optimization-based ILC that allows for enforcing additional
structure, including sparsity. The proposed method enforces sparsity in a
generalized setting through convex relaxations using norms. The
proposed ILC framework is applied to the optimization of sampling sequences for
resource efficient implementation, trial-varying disturbance attenuation, and
basis function selection. The framework has a large potential in control
applications such as mechatronics, as is confirmed through an application on a
wafer stage.Comment: 12 pages, 14 figure
Learning feedforward controller for a mobile robot vehicle
This paper describes the design and realisation of an on-line learning posetracking controller for a three-wheeled mobile robot vehicle. The controller consists of two components. The first is a constant-gain feedback component, designed on the basis of a second-order model. The second is a learning feedforward component, containing a single-layer neural network, that generates a control contribution on the basis of the desired trajectory of the vehicle. The neural network uses B-spline basis functions, enabling a computationally fast implementation and fast learning. The resulting control system is able to correct for errors due to parameter mismatches and classes of structural errors in the model used for the controller design. After sufficient learning, an existing static gain controller designed on the basis of an extensive model has been outperformed in terms of tracking accuracy
Design and real time implementation of nonlinear minimum variance filter
In this paper, the design and real time implementation of a Nonlinear Minimum Variance (NMV) estimator is presented using a laboratory based ball and beam system. The real time implementation employs a LabVIEW based tool. The novelty of this work lies in the design steps and the practical implementation of the NMV estimation technique which up till now only investigated using simulation studies. The paper also discusses the advantages and limitations of the NMV estimator based on the real time application results. These are compared with results obtained using an extended Kalman filter
Regulation Theory
This paper reviews the design of regulation loops for power converters. Power
converter control being a vast domain, it does not aim to be exhaustive. The
objective is to give a rapid overview of the main synthesis methods in both
continuous- and discrete-time domains.Comment: 23 pages, contribution to the 2014 CAS - CERN Accelerator School:
Power Converters, Baden, Switzerland, 7-14 May 201
Dirty RF Signal Processing for Mitigation of Receiver Front-end Non-linearity
ï»żModerne drahtlose Kommunikationssysteme stellen hohe und teilweise
gegensÀtzliche Anforderungen an die Hardware der Funkmodule, wie z.B.
niedriger Energieverbrauch, groĂe Bandbreite und hohe LinearitĂ€t. Die
GewÀhrleistung einer ausreichenden LinearitÀt ist, neben anderen analogen
Parametern, eine Herausforderung im praktischen Design der Funkmodule. Der
Fokus der Dissertation liegt auf breitbandigen HF-Frontends fĂŒr
Software-konfigurierbare Funkmodule, die seit einigen Jahren kommerziell
verfĂŒgbar sind. Die praktischen Herausforderungen und Grenzen solcher
flexiblen Funkmodule offenbaren sich vor allem im realen Experiment. Eines
der Hauptprobleme ist die Sicherstellung einer ausreichenden analogen
Performanz ĂŒber einen weiten Frequenzbereich. Aus einer Vielzahl an
analogen Störeffekten behandelt die Arbeit die Analyse und Minderung von
NichtlinearitÀten in EmpfÀngern mit direkt-umsetzender Architektur. Im
Vordergrund stehen dabei Signalverarbeitungsstrategien zur Minderung
nichtlinear verursachter Interferenz - ein Algorithmus, der besser unter
"Dirty RF"-Techniken bekannt ist. Ein digitales Verfahren nach der
VorwÀrtskopplung wird durch intensive Simulationen, Messungen und
Implementierung in realer Hardware verifiziert. Um die LĂŒcken zwischen
Theorie und praktischer Anwendbarkeit zu schlieĂen und das Verfahren in
reale Funkmodule zu integrieren, werden verschiedene Untersuchungen
durchgefĂŒhrt. Hierzu wird ein erweitertes Verhaltensmodell entwickelt, das
die Struktur direkt-umsetzender EmpfÀnger am besten nachbildet und damit
alle Verzerrungen im HF- und Basisband erfasst. DarĂŒber hinaus wird die
LeistungsfÀhigkeit des Algorithmus unter realen Funkkanal-Bedingungen
untersucht. ZusÀtzlich folgt die Vorstellung einer ressourceneffizienten
Echtzeit-Implementierung des Verfahrens auf einem FPGA. AbschlieĂend
diskutiert die Arbeit verschiedene Anwendungsfelder, darunter spektrales
Sensing, robuster GSM-Empfang und GSM-basiertes Passivradar. Es wird
gezeigt, dass nichtlineare Verzerrungen erfolgreich in der digitalen
DomÀne gemindert werden können, wodurch die Bitfehlerrate gestörter
modulierter Signale sinkt und der Anteil nichtlinear verursachter
Interferenz minimiert wird. SchlieĂlich kann durch das Verfahren die
effektive LinearitÀt des HF-Frontends stark erhöht werden. Damit wird der
zuverlÀssige Betrieb eines einfachen Funkmoduls unter dem Einfluss der
EmpfÀngernichtlinearitÀt möglich. Aufgrund des flexiblen Designs ist der
Algorithmus fĂŒr breitbandige EmpfĂ€nger universal einsetzbar und ist nicht
auf Software-konfigurierbare Funkmodule beschrÀnkt.Today's wireless communication systems place high requirements on the
radio's hardware that are largely mutually exclusive, such as low power
consumption, wide bandwidth, and high linearity. Achieving a sufficient
linearity, among other analogue characteristics, is a challenging issue in
practical transceiver design. The focus of this thesis is on wideband
receiver RF front-ends for software defined radio technology, which became
commercially available in the recent years. Practical challenges and
limitations are being revealed in real-world experiments with these radios.
One of the main problems is to ensure a sufficient RF performance of the
front-end over a wide bandwidth. The thesis covers the analysis and
mitigation of receiver non-linearity of typical direct-conversion receiver
architectures, among other RF impairments. The main focus is on DSP-based
algorithms for mitigating non-linearly induced interference, an approach
also known as "Dirty RF" signal processing techniques. The conceived
digital feedforward mitigation algorithm is verified through extensive
simulations, RF measurements, and implementation in real hardware. Various
studies are carried out that bridge the gap between theory and practical
applicability of this approach, especially with the aim of integrating that
technique into real devices. To this end, an advanced baseband behavioural
model is developed that matches to direct-conversion receiver architectures
as close as possible, and thus considers all generated distortions at RF
and baseband. In addition, the algorithm's performance is verified under
challenging fading conditions. Moreover, the thesis presents a
resource-efficient real-time implementation of the proposed solution on an
FPGA. Finally, different use cases are covered in the thesis that includes
spectrum monitoring or sensing, GSM downlink reception, and GSM-based
passive radar. It is shown that non-linear distortions can be successfully
mitigated at system level in the digital domain, thereby decreasing the bit
error rate of distorted modulated signals and reducing the amount of
non-linearly induced interference. Finally, the effective linearity of the
front-end is increased substantially. Thus, the proper operation of a
low-cost radio under presence of receiver non-linearity is possible. Due to
the flexible design, the algorithm is generally applicable for wideband
receivers and is not restricted to software defined radios
Artificial Neural Networks
Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.
On the computation of -flat outputs for differential-delay systems
We introduce a new definition of -flatness for linear differential delay
systems with time-varying coefficients. We characterize - and -0-flat
outputs and provide an algorithm to efficiently compute such outputs. We
present an academic example of motion planning to discuss the pertinence of the
approach.Comment: Minor corrections to fit with the journal versio
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