Dissertations / Theses on the topic 'Adaptive algorithms'
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1
Abu-Bakar, Nordin. "Adaptive genetic algorithms." Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343268.
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2
Mirzazadeh, Mehdi. "Adaptive Comparison-Based Algorithms for Evaluating Set Queries." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1147.
Full textAbstract:
In this thesis we study a problem that arises in answering boolean queries submitted to a search engine. Usually a search engine stores the set of IDs of documents containing each word in a pre-computed sorted order and to evaluate a query like "computer AND science" the search engine has to evaluate the union of the sets of documents containing the words "computer" and "science". More complex queries will result in more complex set expressions. In this thesis we consider the problem of evaluation of a set expression with union and intersection as operators and ordered sets as operands. We explore properties of comparison-based algorithms for the problem. A proof of a set expression is the set of comparisons that a comparison-based algorithm performs before it can determine the result of the expression. We discuss the properties of the proofs of set expressions and based on how complex the smallest proofs of a set expression E are, we define a measurement for determining how difficult it is for E to be computed. Then, we design an algorithm that is adaptive to the difficulty of the input expression and we show that the running time of the algorithm is roughly proportional to difficulty of the input expression, where the factor is roughly logarithmic in the number of the operands of the input expression.
3
Vlajic, Natalija J. "Adaptive algorithms for hypertext clustering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ32276.pdf.
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Sequeira, Armando M. P. de Jesus. "Adaptive two dimensional RLS algorithms." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25653.
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5
Shah, Ijteba-ul-Hasnain. "Constrained adaptive natural gradient algorithms for adaptive array processing." Thesis, University of Strathclyde, 2011. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15646.
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6
Stone, Joseph Carlyle. "Adaptive discrete-ordinates algorithms and strategies." Texas A&M University, 2007. http://hdl.handle.net/1969.1/85857.
Full textAbstract:
The approaches for discretizing the direction variable in particle transport
calculations are the discrete-ordinates method and function-expansion methods. Both
approaches are limited if the transport solution is not smooth.
Angular discretization errors in the discrete-ordinates method arise from the inability
of a given quadrature set to accurately perform the needed integrals over the direction
("angular") domain. We propose that an adaptive discrete-ordinate algorithm will be
useful in many problems of practical interest. We start with a "base quadrature set" and
add quadrature points as needed in order to resolve the angular flux function. We
compare an interpolated angular-flux value against a calculated value. If the values are
within a user specified tolerance, the point is not added; otherwise it is. Upon the
addition of a point we must recalculate weights.
Our interpolatory functions map angular-flux values at the quadrature directions to a
continuous function that can be evaluated at any direction. We force our quadrature
weights to be consistent with these functions in the sense that the quadrature integral of
the angular flux is the exact integral of the interpolatory function (a finite-element methodology that determines coefficients by collocation instead of the usual weightedresidual
procedure).
We demonstrate our approach in two-dimensional Cartesian geometry, focusing on
the azimuthal direction The interpolative methods we test are simple linear, linear in
sine and cosine, an Abu-Shumays â baseâ quadrature with a simple linear adaptive and an
Abu-Shumays â baseâ quadrature with a linear in sine and cosine adaptive. In the latter
two methods the local refinement does not reduce the ability of the base set to integrate
high-order spherical harmonics (important in problems with highly anisotropic
scattering).
We utilize a variety of one-group test problems to demonstrate that in all cases,
angular discretization errors (including "ray effects") can be eliminated to whatever
tolerance the user requests. We further demonstrate through detailed quantitative
analysis that local refinement does indeed produce a more efficient placement of
unknowns.
We conclude that this work introduces a very promising approach to a long-standing
problem in deterministic transport, and we believe it will lead to fruitful avenues of
further investigation.
7
Bate, Stephen Donald. "Adaptive coding algorithms for data transmission." Thesis, Coventry University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303388.
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Korejo, Imtiaz Ali. "Adaptive mutation operators for evolutionary algorithms." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10315.
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Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.
9
Wilstrup, Steven L. "Adaptive algorithms for two dimensional filtering." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22855.
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10
Nambiar, Raghu. "Learning algorithms for adaptive digital filtering." Thesis, Durham University, 1993. http://etheses.dur.ac.uk/5544/.
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In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme.
11
Gurrapu, Omprakash. "Adaptive filter algorithms for channel equalization." Thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-19219.
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Equalization techniques compensate for the time dispersion introduced bycommunication channels and combat the resulting inter-symbol interference (ISI) effect.Given a channel of unknown impulse response, the purpose of an adaptive equalizer is tooperate on the channel output such that the cascade connection of the channel and theequalizer provides an approximation to an ideal transmission medium. Typically,adaptive equalizers used in digital communications require an initial training period,during which a known data sequence is transmitted. A replica of this sequence is madeavailable at the receiver in proper synchronism with the transmitter, thereby making itpossible for adjustments to be made to the equalizer coefficients in accordance with theadaptive filtering algorithm employed in the equalizer design. This type of equalization isknown as Non-Blind equalization. However, in practical situations, it would be highlydesirable to achieve complete adaptation without access to a desired response. Clearly,some form of Blind equalization has to be built into the receiver design. Blind equalizerssimultaneously estimate the transmitted signal and the channel parameters, which mayeven be time-varying. The aim of the project is to study the performance of variousadaptive filter algorithms for blind channel equalization through computer simulations.Uppsatsnivå: D
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Lincoln, Andrea (Andrea I. ). "Analysis of recursive cache-adaptive algorithms." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100630.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Title as it appears in MIT Commencement Exercises program, June 5, 2015: Advances in cache analysis for algorithms. Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 33-34).
The performance and behavior of caches is becoming increasingly important to the overall performance of systems. As a result, there has been extensive study of caching in theoretical computer science. The traditionally studied model was the external-memory model [AV88]. In this model cache misses cost O(1) and operations on the CPU are free [AV88]. In 1999 Frigo, Leiserson, Prokop and Ramachandran proposed the cache-oblivious model [FLPR99]. In this model algorithms don't have access to cache information, like the size of the cache. However, neither model captures the fact that an algorithm's available cache can change over time, which can effect its efficiency. In 2014, the cache-adaptive model was proposed [BEF+14]. The cache-adaptive model is a model where the cache can change in size when a cache miss occurs [BEF+14]. In more recent work, to be published, methods for analysis in the cache-adaptive context are proposed [MABM]. In this thesis we analyze the efficiency of recursive algorithms in the cache-adaptive model. Specifically, we present lower bounds on progress per cache miss and upper bounds on the total number of cache misses in an execution. The algorithms we analyze are divide and conquer algorithms that follow the recurrence T(N) = aT (N=b) + Nc. For divide and conquer algorithms of this form, there is a method for calculating within a constant factor the number of cache misses incurred. This provides a theorem analogues to the Master Theorem, but applicable to the cache-adaptive model.
by Andrea Lincoln.
M. Eng.
13
Niemeyer, Günter Dieter. "Computational algorithms for adaptive robot control." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/42187.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1990.Title as it appeared in MIT Graduate list, February 1990: Computational algorithms for adaptive control.
Includes bibliographical references (leaves 87-91).
by Günter [sic] Dieter Niemeyer.
M.S.
14
Moore, Anne M. "Adaptive algorithms for nonstationary time series." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/12678.
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Nonstationary time series arise in many different disciplines, and there are many different reasons for wishing to study them. The particular interest in this thesis is in modelling the time series so as to obtain certain parameters of interest from it. Whatever the reason for studying such a time series and whatever the method chosen, in order to accommodate the nonstationarity of the series it is important to use an adaptive algorithm whose parameters are permitted to vary with time. The first aim of this thesis will be to examine existing adaptive algorithms, highlighting their strengths and weaknesses to determine which, if any, offers the best way forward towards developing new algorithms. Following this, rather than consider a specific class of algorithm a generic algorithm which contains the properties of more than one class of algorithm will be examined. To facilitate the development of this algorithm hyperparameters and hypermodels will be introduced. Results of simulations run to test the algorithms performance will be given. The second aim of this thesis will be to develop a new algorithm, the fast adaptive forward backward least squares algorithm. This algorithm incorporates a 'forgetting factor' to enable the tracking of nonstationary signals. Simulations will be performed which show that the algorithm can outperform the unwindowed version in the presence of a nonstationary signal. Stabilization techniques will be introduced which will prevent the algorithm exhibiting numerical instabilities to which this type of algorithm are prone. Simulations results will be presented to give guidelines for the choice of values of feedback gains which are to be used to prevent the exhibition of instability. Finally the advantages and limitations of both the new and existing algorithms will be summarized and suggested areas of future research outlined.
15
Pratap, Amrit Abu-Mostafa Yaser S. Abu-Mostafa Yaser S. "Adaptive learning algorithms and data cloning /." Diss., Pasadena, Calif. : Caltech, 2008. http://resolver.caltech.edu/CaltechETD:etd-05292008-231048.
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Riedlbeck, Rita. "Adaptive algorithms for poromechanics and poroplasticity." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS055/document.
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Dans cette thèse nous développons des estimations d'erreur a posteriori par équilibrage de flux pour la poro-mécanique et la poro-plasticité.En se basant sur ces estimations, nous proposons des algorithmes adaptatifs pour la résolution numérique de problèmes en mécanique des sols.Le premier chapitre traite des problèmes en poro-élasticité linéaire.Nous obtenons une borne garantie sur l'erreur en utilisant des reconstructions équilibrées et $H({rm div})$-conformes de la vitesse de Darcy et du tenseur de contraintes mécaniques.Nous appliquons cette estimation dans un algorithme adaptif pour équilibrer les composantes de l'erreur provenant de la discrétisation en espace et en temps pour des simulations en deux dimensions.La contribution principale du chapitre porte sur la reconstruction symétrique du tenseur de contraintes.Dans le deuxième chapitre nous proposons une deuxième technique de reconstruction du tenseur de contraintes dans le cadre de l'élasticité nonlinéaire.En imposant la symétrie faiblement, cette technique améliore les temps de calcul et facilite l'implémentation.Nous démontrons l'éfficacité locale et globale des estimateurs obtenus avec cette reconstruction pour une grande classe de lois en hyperélasticité.En ajoutant un estimateur de l'erreur de linéarisation, nous introduisons des critères d'arrêt adaptatifs pour le solveur de linéarisation.Le troisième chapitre est consacré à l'application industrielle des résultats obtenus. Nous appliquons un algorithme adaptatif à des problèmes poro-mécaniques en trois dimensions avec des lois de comportement mécanique élasto-plastiquesIn this Ph.D. thesis we develop equilibrated flux a posteriori error estimates for poro-mechanical and poro-plasticity problems.Based on these estimations we propose adaptive algorithms for the numerical solution of problems in soil mechanics.The first chapter deals with linear poro-elasticity problems.Using equilibrated $H({rm div})$-conforming flux reconstructions of the Darcy velocity and the mechanical stress tensor, we obtain a guaranteed upper bound on the error.We apply this estimate in an adaptive algorithm balancing the space and time discretisation error components in simulations in two space dimensions.The main contribution of this chapter is the symmetric reconstruction of the stress tensor.In the second chapter we propose another reconstruction technique for the stress tensor, while considering nonlinear elasticity problems.By imposing the symmetry of the tensor only weakly, we reduce computation time and simplify the implementation.We prove that the estimate obtained using this stress reconstuction is locally and globally efficient for a wide range of hyperelasticity problems.We add a linearization error estimator, enabling us to introduce adaptive stopping criteria for the linearization solver.The third chapter adresses the industrial application of the obtained results.We apply an adaptive algorithm to three-dimensional poro-mechanical problems involving elasto-plastic mechanical behavior laws
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Sridharan, M. K. "Subband Adaptive Filtering Algorithms And Applications." Thesis, Indian Institute of Science, 2000. https://etd.iisc.ac.in/handle/2005/266.
Full textAbstract:
In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized.
This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems.
Details of the work
To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable
for applications like acoustic echo cancellation.
The filtered output of the modified generalized fast filtering structure is given by
(formula)
where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters.
Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property
(formula)
can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate.
PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement .
Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations.
Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme.
Conclusions
Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart.
(Refer PDF file for Formulas)
18
Sridharan, M. K. "Subband Adaptive Filtering Algorithms And Applications." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/266.
Full textAbstract:
In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized.
This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems.
Details of the work
To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable
for applications like acoustic echo cancellation.
The filtered output of the modified generalized fast filtering structure is given by
(formula)
where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters.
Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property
(formula)
can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate.
PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement .
Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations.
Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme.
Conclusions
Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart.
(Refer PDF file for Formulas)
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Gómez, Pablo Emilio Jojoa. "Um algoritmo acelerador de parâmetros." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-17122003-163354/.
Full textAbstract:
No campo do processamento digital de sinais e em especial da filtragem adaptativa, procura-se continuamente algoritmos que sejam rápidos e simples. Neste contexto, este trabalho apresenta o estudo de novos algoritmos de tempo discreto denominados algoritmos aceleradores (completo, regressivo e progressivo), obtidos a partir da discretização de um algoritmo de tempo contínuo baseado no ajuste da segunda derivada (aceleração) da estimativa dos parâmetros. Destes algoritmos optou-se por estudar mais aprofundadamente os algoritmos aceleradores progressivo e regressivo, devido respectivamente a sua menor complexidade computacional e ao seu desempenho. Para este estudo e análise foram escolhidos como base de comparação os algoritmos LMS e NLMS. Isto porque estes algoritmos estão entre os mais usados e, assim como os algoritmos aceleradores, podem ser obtidos a partir da discretização de algoritmos de tempo contínuo através dos métodos de Euler progressivo e regressivo respectivamente. A análise do algoritmo progressivo mostrou que seu desempenho é inferior ao do algoritmo LMS. Visando diminuir a complexidade computacional do algoritmo acelerador regressivo, foi obtido um novo algoritmo: o versão g. Assim a análise focou-se no algoritmo acelerador regressivo versão g, o qual apresentou um desempenho bom quando comparado no desajuste e no tracking com o algoritmo NLMS, mostrando um melhor compromisso entre velocidade de convergência e variância das estimativas. Este bom desempenho foi comprovado por análises teóricas, por simulações e através da aplicação deste algoritmo na equalização de um canal variante no tempo.In the digital signal processing field and specially in adaptive filtering, there is a constant search for algorithms both simple and with good performance. This work presents new discrete-time algorithms called accelerating algorithms (APCM and ARg), obtained through the discretization of a continuous-time algorithm that uses the second derivate (acceleration) to adjust the parameter estimates. We provide theoretical analyses for both algorithms, finding expressions for the mean and mean-square errors in the parameter estimates. In addition, we compare the performance of the accelerating algorithms with LMS and NLMS. The analysis of the APCM algorithm showed that its performance is inferior to that of the LMS algorithm. On the other hand, the ARg algorithm presented good performance when compared in terms of misadjustment and tracking with the NLMS algorithm, showing a better compromise between convergence speed and variance of the estimates. This better performance was proven by theoretical analyses, by simulations and through the application of this algorithm to the equalization of a time-variant channel.
20
Mayfield, Andrew James. "Adaptive mesh refinement." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358687.
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au, Daniel Schubert@csiro, and Daniel Schubert. "A Multivariate Adaptive Trimmed Likelihood Algorithm." Murdoch University, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20061019.132720.
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The research reported in this thesis describes a new algorithm which can be used to
robustify statistical estimates adaptively. The algorithm does not require any pre-specified
cut-off value between inlying and outlying regions and there is no presumption of any
cluster configuration. This new algorithm adapts to any particular sample and may advise
the trimming of a certain proportion of data considered extraneous or may divulge the
structure of a multi-modal data set. Its adaptive quality also allows for the confirmation
that uni-modal, multivariate normal data sets are outlier free. It is also shown to behave
independently of the type of outlier, for example, whether applied to a data set with a
solitary observation located in some extreme region or to a data set composed of clusters
of outlying data, this algorithm performs with a high probability of success.
22
Law, Nga Lam. "Parameter-free adaptive genetic algorithm /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202007%20LAW.
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Zhang, Jie. "Blind adaptive cyclic filtering and beamforming algorithms /." *McMaster only, 2001.
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Cudnoch, Martin. "Efficient adaptive loading algorithms for multicarrier modulation." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98953.
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Communication networks rely increasingly upon wireless systems to provide transmission over the "last mile". This is due not only to the low cost of infrastructures, but also, and perhaps to a larger extent, to their flexibility. Increasingly, the current applications put a large burden on the throughput of those wireless systems. Multicarrier modulation has proven a highly effective technique to sustain such throughput and is now used in numerous current and future standards such as IEEE 802.11a. To take full advantage of that scheme, however, the multicarrier system needs to adapt to the transmission channel's time varying parameter. This can be achieved by adaptive loading algorithms, which unfortunately, are usually computationally expensive.This work addresses the issue of computational complexity within an adaptive loading algorithm by concentrating on the optimization of two algorithms crucial for a multicarrier system's performance: equalization and bit loading. Firstly, a variable-length equalizer algorithm is optimized and modified to make fast large-scale simulations possible. The algorithm is bound to significantly outperform fixed-length schemes of comparable complexity in terms of probability of error of the system. Secondly, an adaptive bit loading algorithm is implemented in real-time. The implementation target is a fixed-point DSP. The algorithm is optimized and an alternate, more computationally efficient version is proposed. The implementation is then tested for robustness and speed of convergence. Both versions of the algorithm converge to a solution well within the time constraint, with the proposed version offering a clearly better performance.
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Benson, Maja. "Adaptive space diversity algorithms for mobile communications." Thesis, Staffordshire University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263965.
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Papoulis, Eftychios. "Structures and algorithms for subband adaptive filtering." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429497.
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Lambotharan, Sangarapillai. "Algorithms and structures for adaptive blind equalization." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268038.
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Pazaitis, Dimitrios I. "Performance improvement in adaptive signal processing algorithms." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368771.
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Dang, Hieu. "Adaptive multiobjective memetic optimization: algorithms and applications." Journal of Cognitive Informatics and Natural Intelligence, 2012. http://hdl.handle.net/1993/30856.
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The thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results.
This thesis also presents two applications of these algorithms. First, an optimal image data hiding technique has been formulated as a multiobjective optimization problem with conflicting objectives. In particular, trade-off factors in designing an optimal image data hiding are investigated to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and the adaptive multiobjective memetic optimization algorithm (AMMOA) to solve this challenging problem. This novel image data hiding approach has been implemented for many different test natural images with remarkable robustness and transparency of the embedded logo watermark. We also introduce a perceptual measure based on the relative Rényi information spectrum to evaluate the quality of watermarked images.
The second application is the problem of joint spectrum sensing and power control optimization for a multichannel, multiple-user cognitive radio network. We investigated trade-off factors in designing efficient spectrum sensing techniques to maximize the throughput and minimize the interference. To maximize the throughput of secondary users and minimize the interference to primary users, we propose a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is used again in the form of AMMOA. This algorithm learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference to the cognitive radio network.February 2016
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Egaña, Iztueta Lander, and Martínez Javier Roda. "Function Block Algorithms for Adaptive Robotic Control." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9733.
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The purpose of this project is the creation of an adaptive Function Block control system, and the implementation of Artificial Intelligence integrated within the Function Block control system, using IEC 61499 standard to control an ABB 6-axis virtual robot, simulated in the software RobotStudio. To develop these objectives, we studied a lot of necessary concepts and how to use three different softwares. To learn how to use the softwares, some tests were carried out. RobotStudio is a program developed by ABB Robotics Company where an ABB robot and a station are simulated. There, we designed and created a virtual assembly cell with the virtual IRB140 robot and the necessary pieces to simulate the system. To control the robot and the direct access to the different tools of RobotStudio, it is necessary to use an application programming interface (API) developed by ABB Robotics Company. C sharp (C#) language is used to program using the API, but this language is not supported by the Function Block programming software nxtStudio. Because of this, we used VisualStudio software. In this software, we use the API libraries to start and stop the robot and load a RAPID file in the controller. In a RAPID file the instructions that the robot must follow are written. So, we had to learn about how to program in C# language and how to use VisualStudio software. Also, to learn about IEC 61499 standard it was necessary to read some books. This standard determines how an application should be programmed through function blocks. A function block is a unit of program with a certain functionality which contains data and variables that can be manipulated in the same function block by several algorithms. To program in this standard we learnt how to use nxtStudio software, consuming a lot of time because the program is quite complex and it is not much used in the industrial world yet. Some tests were performed to learn different programing skills in this standard, such as how to use UDP communication protocol and how to program interfaces. Learning UDP communication was really useful because it is necessary for communication between nxtStudio and other programs, and also learning how to use interfaces to let the user access the program. Once we had learnt about how to use and program the different softwares and languages, we began to program the project. Then, we had some troubles with nxtStudio because strings longer than fourteen characters cannot be used here. So, a motion alarm was developed in VisualStudio program. And another important limitation of nxtStudio is that C++ language cannot be used. Therefore, the creation of an Artificial Intelligence system was not possible. So, we created a Function Block control system. This system is a logistical system realised through loops, conditions and counters. All this makes the robot more adaptive. As the AI could not be carried out because of the different limitations, we theoretically designed the AI system. It will be possible to implement the AI when the limitations and the problems are solved.
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Palmer, Alexander S. "Adaptive image restoration algorithms using intelligent techniques." Thesis, University of East Anglia, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405233.
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Keratiotis, George. "Adaptive algorithms for real-time noise cancellation." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324215.
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Talebi, Sayedpouria. "Adaptive filtering algorithms for quaternion-valued signals." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/44568.
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Advances in sensor technology have made possible the recoding of three and four-dimensional signals which afford a better representation of our actual three-dimensional world than the ''flat view'' one and two-dimensional approaches. Although it is straightforward to model such signals as real-valued vectors, many applications require unambiguous modeling of orientation and rotation, where the division algebra of quaternions provides crucial advantages over real-valued vector approaches. The focus of this thesis is on the use of recent advances in quaternion-valued signal processing, such as the quaternion augmented statistics, widely-linear modeling, and the HR-calculus, in order to develop practical adaptive signal processing algorithms in the quaternion domain which deal with the notion of phase and frequency in a compact and physically meaningful way. To this end, first a real-time tracker of quaternion impropriety is developed, which allows for choosing between strictly linear and widely-linear quaternion-valued signal processing algorithms in real-time, in order to reduce computational complexity where appropriate. This is followed by the strictly linear and widely-linear quaternion least mean phase algorithms that are developed for phase-only estimation in the quaternion domain, which is accompanied by both quantitative performance assessment and physical interpretation of operations. Next, the practical application of state space modeling of three-phase power signals in smart grid management and control systems is considered, and a robust complex-valued state space model for frequency estimation in three-phase systems is presented. Its advantages over other available estimators are demonstrated both in an analytical sense and through simulations. The concept is then expanded to the quaternion setting in order to make possible the simultaneous estimation of the system frequency and its voltage phasors. Furthermore, a distributed quaternion Kalman filtering algorithm is developed for frequency estimation over power distribution networks and collaborative target tracking. Finally, statistics of stable quaternion-valued random variables, that include quaternion-valued Gaussian random variables as a special case, is investigated in order to develop a framework for the modeling and processing of heavy-tailed quaternion-valued signals.
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Sathe, Vinay Padmakar Vaidyanathan P. P. Vaidyanathan P. P. "Multirate adaptive filtering algorithms : analysis and applications /." Diss., Pasadena, Calif. : California Institute of Technology, 1991. http://resolver.caltech.edu/CaltechETD:etd-07122007-103754.
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Hussain, Zahir M. "Adaptive instantaneous frequency estimation: Techniques and algorithms." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36137/7/36137_Digitised%20Thesis.pdf.
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This thesis deals with the problem of the instantaneous frequency (IF) estimation of sinusoidal signals. This topic plays significant role in signal processing and communications.
Depending on the type of the signal, two major approaches are considered. For IF estimation of single-tone or digitally-modulated sinusoidal signals (like frequency shift keying signals) the approach of digital phase-locked loops (DPLLs) is considered, and this is Part-I of this thesis. For FM signals the approach of time-frequency analysis is considered, and this is Part-II of the thesis.
In part-I we have utilized sinusoidal DPLLs with non-uniform sampling scheme as this type is widely used in communication systems. The digital tanlock loop (DTL) has introduced significant advantages over other existing DPLLs. In the last 10 years many efforts have been made to improve DTL performance. However, this loop and all of its modifications utilizes Hilbert transformer (HT) to produce a signal-independent 90-degree phase-shifted version of the input signal. Hilbert transformer can be realized approximately using a finite impulse response (FIR) digital filter. This realization introduces further complexity in the loop in addition to approximations and frequency limitations on the input signal. We have tried to avoid practical difficulties associated with the conventional tanlock scheme while keeping its advantages. A time-delay is utilized in the tanlock scheme of DTL to produce a signal-dependent phase shift. This gave rise to the time-delay digital tanlock loop (TDTL). Fixed point theorems are used to analyze the behavior of the new loop. As such TDTL combines the two major approaches in DPLLs: the non-linear approach of sinusoidal DPLL based on fixed point analysis, and the linear tanlock approach based on the arctan phase detection. TDTL preserves the main advantages of the DTL despite its reduced structure. An application of TDTL in FSK demodulation is also considered. This idea of replacing HT by a time-delay may be of interest in other signal processing systems. Hence we have analyzed and compared the behaviors of the HT and the time-delay in the presence of additive Gaussian noise.
Based on the above analysis, the behavior of the first and second-order TDTLs has been analyzed in additive Gaussian noise.
Since DPLLs need time for locking, they are normally not efficient in tracking the continuously changing frequencies of non-stationary signals, i.e. signals with time-varying spectra. Nonstationary signals are of importance in synthetic and real life applications. An example is the frequency-modulated (FM) signals widely used in communication systems. Part-II of this thesis is dedicated for the IF estimation of non-stationary signals. For such signals the classical spectral techniques break down, due to the time-varying nature of their spectra, and more advanced techniques should be utilized.
For the purpose of instantaneous frequency estimation of non-stationary signals there are two major approaches: parametric and non-parametric. We chose the non-parametric approach which is based on time-frequency analysis. This approach is computationally less expensive and more effective in dealing with multicomponent signals, which are the main aim of this part of the thesis.
A time-frequency distribution (TFD) of a signal is a two-dimensional transformation of the signal to the time-frequency domain. Multicomponent signals can be identified by multiple energy peaks in the time-frequency domain. Many real life and synthetic signals are of multicomponent nature and there is little in the literature concerning IF estimation of such signals. This is why we have concentrated on multicomponent signals in Part-H.
An adaptive algorithm for IF estimation using the quadratic time-frequency distributions has been analyzed. A class of time-frequency distributions that are more suitable for this purpose has been proposed. The kernels of this class are time-only or one-dimensional, rather than the time-lag (two-dimensional) kernels. Hence this class has been named as the T -class. If the parameters of these TFDs are properly chosen, they are more efficient than the existing fixed-kernel TFDs in terms of resolution (energy concentration around the IF) and artifacts reduction. The T-distributions has been used in the IF adaptive algorithm and proved to be efficient in tracking rapidly changing frequencies. They also enables direct amplitude estimation for the components of a multicomponent
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Cheung, Bing-Leung Patrick. "Simulation of Adaptive Array Algorithms for OFDM and Adaptive Vector OFDM Systems." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34915.
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The increasing demand for high data rate services necessitates the adoption of very wideband waveforms. In this case, the channel is frequency-selective, that is, a large number of resolvable multipaths are present in this environment and fading is not highly correlated across the band. Orthogonal frequency division multiplexing (OFDM) is well-known to be effective against multipath distortion. It is a multicarrier communication scheme, in which the bandwidth of the channel is divided into subcarriers and data symbols are modulated and transmitted on each subcarrier simultaneously. By inserting guard time that is longer than the delay spread of the channel, an OFDM system is able to mitigate intersymbol interference (ISI). Deploying an adaptive antenna array at the receiver can help separate the desired signal from interfering signals which originate from different spatial locations. This enhancement of signal integrity increases system capacity. In this research, we apply adaptive array algorithms to OFDM systems and study their performance in a multipath environment with the presence of interference. A novel adaptive beamforming algorithm based on the minimum mean-squared error (MMSE) criterion, which is referred to as frequency-domain beamforming, is proposed that exploits the characteristics of OFDM signals. The computational complexity of frequency-domain beamforming is also studied. Simulation results show employing an adaptive antenna array with an OFDM system significantly improves system performance when interference is present. Simulations also show that the computational complexity of the algorithm can be reduced by half without significant performance degradation. Adaptive array algorithms based on the maximum signal-to-noise ratio (MSNR) and the maximum signal-to-interference-plus-noise ratio (MSINR) criteria are also applied to adaptive vector OFDM systems (AV-OFDM). Simulation results show that the adaptive algorithm based on the MSNR criterion has superior performance in the multipath environment but performs worse than the one based on the MSINR criterion under the flat fading channel.Master of Science
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Rong, Zhigang. "Simulation of Adaptive Array Algorithms for CDMA Systems." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-09182008-063401/.
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Turner, Steven Primitivo. "Adaptive out of step relay algorithm." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-01242009-063244/.
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Huang, Yuchen. "Adaptive Notch Filter." PDXScholar, 1994. https://pdxscholar.library.pdx.edu/open_access_etds/4802.
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The thesis presents a new adaptive notch filter (ANF) algorithm that is more accurate and efficient and has a faster convergent rate than previous ANF algorithms. In 1985, Nehorai designed an infinite impulse response (UR) ANF algorithm that has many advantages over previous ANF algorithms. It requires a minimal number of parameters with constrained poles and zeros. It has higher stability and sharper notches than any ANF algorithm until now. Because of the special filter structure and the recursive prediction error (RPE) method, however, the algorithm is very sensitive to the initial estimate of the filter coefficient and its covariance. Furthermore, convergence to the true filter coefficient is not guaranteed since the error-performance surface of the filter has its global minimum lying on a fairly flat region. We propose a new ANF algorithm that overcomes the convergence problem. By choosing a smaller notch bandwidth control parameter that makes the error-performance surface less flat, we can more easily detect a global minimum. We also propose a new convergence criterion to be used with the algorithm and a self-adjustment feature to reset the initial estimate of the filter coefficient and its covariance. This results in guaranteed convergence with more accurate results and more efficient computations than previous ANF algorithms.
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Lai, Ching-An. "Global optimization algorithms for adaptive infinite impulse response filters." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000558.
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Biswas, Mainak. "Content adaptive video processing algorithms for digital TV /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3189792.
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Moon, Kyoung-Sook. "Adaptive Algorithms for Deterministic and Stochastic Differential Equations." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3586.
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Chen, Bingwei. "Adaptive watermarking algorithms for MP3 compressed audio signals." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27963.
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MPEG-1 Layer 3, known as MP3, has generated a significant popularity for distributing digital music over the Internet. MP3 compresses digital music with high ratio while keeping high sound quality. However, copyright issue is raised because of illegal copy, redistribution and various malicious attacks.
Digital watermarking is a technology that allows users to embed some imperceptible data into digital contents such as image, movie and audio data. Once a watermark is embedded into the original MP3 signal, it can be used to identify the copyright holder in order to prevent illegal copy and to verify the modification from the original content.
This thesis presents two novel adaptive watermarking algorithms for MP3 compressed audio signals for copyright protection. Based on Human Auditory System, the proposed algorithms calculate the energy of the original audio signal and apply Gaussian analysis on MP3 frames to adaptively adjust the watermarking coefficients. Watermark is embedded adaptively and transparently during the MP3 compression. The first watermarking algorithm detects watermark based on Gaussian distribution analysis. To enhance the security of the watermark, the second watermarking algorithm embeds random watermark pattern and uses correlation coefficient to detect watermark. Both algorithms support blind watermark detection and perform well. The first algorithm is more robust while the second algorithm is more secure.
LAME 3.96.2 open source was used as standard ISO MP3 encoder and decoder reference in this study. The experimental results show that the proposed watermarking algorithms can work on a variety of audio signals and survive most common signal manipulation and malicious attacks. As expected, the watermarking algorithms provide superior performance on MP3 compression.
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Wongsavengwate, Pisamai. "Adaptive dispatching using genetic algorithms for multiple resources." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1184598551.
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Chung, Jong-Sun. "Fast Power Allocation Algorithms for Adaptive MIMO Systems." Thesis, University of Canterbury. Electrical and Computer Engineering, 2009. http://hdl.handle.net/10092/3764.
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Recent research results have shown that the MIMO wireless communication architecture is a promising approach to achieve high bandwidth efficiencies.
MIMO wireless channels can be simply defined as a link for which both the
transmitting and receiving ends are equipped with multiple antenna elements.
Adaptive modulation and power allocation could be used to further improve the performance of MIMO systems.
This thesis focuses on developing a fast and high performance power allocation algorithm. Three power allocation algorithms are proposed in this thesis and their performances are compared in various system sizes and transceiver architectures. Among the three algorithms proposed in this thesis, the fast algorithm may be considered as the best power allocation algorithm since the performance of the fast algorithm is almost as good as the fullsearch (optimal)algorithm and the mean processing time is considerably less than the fullsearch algorithm. The fast algorithm achieves about 97.6% agreement with the optimal throughput on average. In addition, the time taken to find the power scaling factors using the fullsearch algorithm is about 2300 times longer than the processing time of the fast algorithm in a 6 x 6 system when the SNR is 20dB.
As an extension to the power allocation process, excess power allocation methods are introduced. Excess power is the unused power during the power
allocation process. The power allocation algorithm allocates power to each received SNR to maximize the throughput of the system whereas the excesspower allocation distributes the excess power to each SNR to improve both the instantaneous and temporal behavior of the system. Five different excess power allocation methods are proposed in this thesis. These methods were simulated in the Rayleigh fading channel with different Doppler frequencies, fD = 10Hz,50Hz and 100Hz, where the ACF of the channel coefficients are given by the Jakes' model. The equal BER improvement method showed a slightly better performance than the other methods. The equal BER improvement method enables the system to maintain the power scaling factors without sacrificing QoS for 19.6 ms on average when the maximum Doppler shift is 10Hz.
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Kabbara, Jad. "Kernel adaptive filtering algorithms with improved tracking ability." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123272.
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In recent years, there has been an increasing interest in kernel methods in areas such as machine learning and signal processing as these methods show strong performance in classification and regression problems. Interesting "kernelized" extensions of many well-known algorithms in artificial intelligence and signal processing have been presented, particularly, kernel versions of the popular online recursive least squares (RLS) adaptive algorithm, namely kernel RLS (KRLS). These algorithms have been receiving significant attention over the past decade in statistical estimation problems, among which those problems involving tracking time-varying systems. KRLS algorithms obtain a non-linear least squares (LS) regressor as a linear combination of kernel functions evaluated at the elements of a carefully chosen subset, called a dictionary, of the received input vectors. As such, the number of coefficients in that linear combination, i.e., the weights, is equal to the size of the dictionary. This coupling between the number of weights and the dictionary size introduces a trade-off. On one hand, a large dictionary would accurately capture the dynamics of the input-output relationship over time. On the other, it has a detrimental effect on the algorithm's ability to track changes in that relationship because having to adjust a large number of weights can significantly slow down adaptation. In this thesis, we present a new KRLS algorithm designed specifically for the tracking of time-varying systems. The key idea behind the proposed algorithm is to break the dependency of the number of weights on the dictionary size. In the proposed method, the number of weights K is fixed and is independent from the dictionary size.Particularly, we use a novel hybrid approach for the construction of the dictionary that employs the so-called surprise criterion for admitting data samples along with a simple pruning method ("remove-the-oldest") that imposes a hard limit on the dictionary size. Then, we propose to construct a K-sparse LS regressor tracking the relationship of the most recent training input-output pairs using the K dictionary elements that provide the best approximation of the output values. Identifying those dictionary elements is a combinatorial optimization problem with a prohibitive computational complexity. To overcome this, we extend the Subspace Pursuit algorithm (SP) which, in essence, is a low complexity method to obtain LS solutions with a pre-specified sparsity level, to non-linear regression problems and introduce a kernel version of SP, which we call Kernel SP (KSP). The standard KRLS is used to recursively update the weights until a new dictionary element selection is triggered by the admission of a new input vector to the dictionary. Simulations show that that the proposed algorithm outperforms existing KRLS-type algorithms in tracking time-varying systems and highly chaotic time series.Au cours des dernières années, il y a eu un intérêt accru pour les méthodes à noyau dans des domaines tels que l'apprentissage automatique et le traitement du signal, puisque ces méthodes démontrent une performance supérieure dans la résolution des problèmes de classification et de régression. D'intéressantes extensions à noyau de plusieurs algorithmes connus en intelligence artificielle et en traitement du signal ont été introduites, particulièrement, les versions à noyau du fameux algorithme d'apprentissage incrémental des moindres carrés récursifs (en anglais, Recursive Least Squares (RLS)), nommées KRLS. Ces algorithmes ont reçu une attention considérable durant la dernière décennie dans les problèmes d'estimation statistique, particulièrement ceux de suivi des systèmes variant dans le temps. Les algorithmes KRLS forment le régresseur aux moindres carrés non-linéaires en utilisant une combinaison linéaire de noyaux évalués aux membres d'un sous-ensemble, appelé dictionnaire, des données d'entrée. Le nombre des coefficients dans la combinaison linéaire, c'est à dire les poids, est égal à la taille du dictionnaire. Ce couplage entre le nombre de poids et la taille du dictionnaire introduit un compromis. D'une part, un dictionnaire de grande taille reflète avec précision la dynamique de la relation entre les données d'entrée et les sorties à travers le temps. De l'autre part, un tel dictionnaire diminue la capacité de l'algorithme à suivre les variations dans cette relation, car ajuster un grand nombre de poids ralentit considérablement l'adaptation de l'algorithme aux variations du système. Dans cette thèse, nous présentons un nouvel algorithme KRLS conçu précisément pour suivre les systèmes variant dans le temps. L'idée principale de l'algorithme est d'enlever la dépendance du nombre de poids sur la taille du dictionnaire. Ainsi, nous proposons de fixer le nombre de poids indépendamment de la taille du dictionnaire.Particulièrement, nous présentons une nouvelle approche hybride pour la construction du dictionnaire qui emploie le test de la surprise pour l'admission des données d'entrées avec une méthode simple d'élagage (l'élimination du membre le plus ancien du dictionnaire) qui impose une limite stricte sur la taille du dictionnaire. Nous proposons ainsi de construire un régresseur "K-creux" (en anglais, K-sparse) aux moindres carrés qui suit la relation des paires de données d'entrées et sorties les plus récentes en utilisant les K membres du dictionnaire qui approximent le mieux possible les sorties. L'identification de ces membres est un problème d'optimisation combinatoire ayant une complexité prohibitive. Pour surmonter cet obstacle, nous étendons l'algorithme Subspace Pursuit (SP), qui est une méthode à complexité réduite pour le calcul des solutions aux moindres carrés ayant un niveau préfixé de parcimonie, aux problèmes de régression non-linéaire. Ainsi, nous introduisons une version à noyau de SP qu'on appelle Kernel Subspace Pursuit (KSP). L'algorithme standard KRLS est utilisé pour l'ajustement récursif des poids jusqu'à ce qu'un nouveau vecteur de donnée soit admis au dictionnaire. Les simulations démontrent que la performance de notre algorithme dans le cadre du suivi des systèmes variant dans le temps surpasse celle d'autres algorithmes KRLS.
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Bosson, Maël. "Adaptive algorithms for computational chemistry and interactive modeling." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00846458.
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At the atomic scale, interactive physically-based modeling tools are more and more in demand. Unfortunately, solving the underlying physics equations at interactive rates is computationally challenging. In this dissertation, we propose new algorithms that allow for interactive modeling of chemical structures. We first present a modeling tool to construct structural models of hydrocarbon systems. The physically-based feedbacks are based on the Brenner potential. In order to be able to interactively edit systems containing numerous atoms, we introduce a new adaptive simulation algorithm. Then, we introduce what we believe to be the first interactive quantum chemistry simulation algorithm at the Atom Superposition and Electron Delocalization Molecular Orbital (ASED-MO) level of theory. This method is based on the divide-and-conquer (D&C) approach, which we show is accurate and efficient for this non-self-consistent semi-empirical theory. We then propose a novel Block-Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. BAQM constrains some nuclei positions and some electronic degrees of freedom on the fly to simplify the simulation. Finally, we demonstrate several applications, including one study of graphane formation, interactive simulation for education purposes, and virtual prototyping at the atomic scale, both on desktop computers and in virtual reality environments.
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Hall, M. C. "Adaptive IIR filter algorithms for real-time applications." Thesis, University of Liverpool, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234800.
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Chan, M. K. "Adaptive signal processing algorithms for non-Gaussian signals." Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269023.
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Ahmeda, Shubat Senoussi. "Adaptive target tracking algorithms for phased array radar." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336953.
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