L'architecture modulaire est une caractéristique distinctive des circuits cérébraux. En particulier, il a été observé l'existence de connexions réciproques entre des zones fonctionnellement interconnectées dans le cortex, et qui par ailleurs sont hiérarchiquement organisées. De plus, le développement évolutif est une autre caractéristique distinctive des espèces vivantes ; même les virus sont capables d'adaptation pour mieux répondre à de nouvelles conditions environnementales. En tenant compte de ces deux importants aspects, nous avons construit un nouvel et unique outil de simulation permettant de modéliser et d'étudier l'évolution des circuits multi-modulaires hiérarchiques. Dans ce modèle, chaque module est représenté par des réseaux de neurones impulsionels et caractérisé à la fois par des changements d'activités neurales imbriquées et par la plasticité synaptique. La morte cellulaire, la plasticité synaptique et l'apoptose intégrés dans le modèle créent des liens auto-associatifs au sein des modules. Ces liens peuvent générer une activité zonale qui reflète l'évolution de la connectivité fonctionnelle à l'intérieur comme à l'extérieur des modules, et donc entre les plusieurs modules neuronaux. L'activité bioélectrique de chaque module est enregistrée au moyen des électrodes virtuelles. Les signaux, electrochipogrammes (EChG), sont analysés par les méthodes fréquentiels et les méthodes de potentiels évoqués afin de trouver des généralités dans le comportement émergeant. En plus de ces méthodes conventionnelles, nous proposons une nouvelle approche de régression non-linéaire structurelle afin de fournir des outils plus puissants et mieux adaptés aux données habituellement analysées dans ce domaine. Nous avons donc testé l'effet d'un stimulus externe sur le développement de liens fonctionnels d'un réseau neuronaux. Le circuit est structuré hiérarchiquement avec un unique module sensoriel et d'autres modules constitués de deux voies parallèles organisées aussi de façon hiérarchique. Nos résultats montrent que les circuits modélisés manifestent un comportement similaire que les circuits biologiques réels. En particulier, tous les éléments du circuit peuvent traiter et maintenir des patterns d'activité liés à la disparition du stimulus. Les résultats obtenus dans nos expériences apportent un éclairage sur les processus émergents et coordonnés de l'activité électrique enregistrée par des EEG de circuits inter-corticaux hiérarchiques et évolutifs qui sont artificiels ou réels. Plus généralement, notre approche concernant les signaux EEG pourrait être étendue à la modélisation d'une vaste variété des processus cognitifs et comportementaux. / Modular architecture is a hallmark of many brain circuits. Particularly, in the cerebral cortex it has been observed that reciprocal connections are often present between functionally interconnected areas that are hierarchically organized. Evolutionary development is another distinctive characteristic of living species, even the simplest viruses are capable to adapt to better fit new environmental conditions. Having hierarchical architectures and evolutionary features in mind, we build unique and novel simulation framework, which allows us to model and to study evolving hierarchically organized circuits of modules of spiking neural networks. Each module is characterized by embedded neural development and expression of spike timing dependent plasticity. Cell death, synaptic plasticity and projection pruning, embedded in the neural model, drive the build-up of auto-associative links within each module, which generate an areal activity that reflect the changes in the corresponding functional connectivity within and between neuronal modules. Bio-electric activity of each module is recorded by means of virtual electrodes and these signals, called electrochipograms (EChG), are analyzed by time and frequency domain methods in order to find general patterns of emerging behavior. Beside time and frequency domain analysis methods, a novel robust non-linear structural regression approach is proposed to provide researchers with more powerful tools specially adapted to the data typically used in the domain. We tested the effect of an external stimulus at fixed frequency fed to a sensory module, which pro jecting its activity to two hierarchically organized parallel pathways. We found that modeled circuits manifest behavior similar in certain aspects to that of real brains. We show evidence that all networks of modules are able to maintain long patterns of activity associated with the stimulus offset. These findings bring new insights to the understanding of EEG-like signals, both real and virtual. The findings prove that the approach is successful and could be extended to model cognitive and behavioral processes in the brains.

A Regressão Robusta surge como uma alternativa ao ajuste por mínimos quadrados quando os erros são contaminados por pontos aberrantes ou existe alguma evidência de violação das suposições do modelo. Na regressão clássica existem critérios de seleção de modelos e medidas de diagnóstico que são muito conhecidos. O objetivo deste trabalho é apresentar os principais critérios robustos de seleção de modelos e medidas de detecção de pontos aberrantes, assim como analisar e comparar o desempenho destes de acordo com diferentes cenários para determinar quais deles se ajustam melhor a determinadas situações. Os critérios de validação cruzada usando simulações de Monte Carlo e o Critério de Informação Bayesiano são conhecidos por desenvolver-se de forma adequada na identificação de modelos. Na dissertação confirmou-se este fato e além disso, suas alternativas robustas também destacam-se neste aspecto. A análise de resíduos constitui uma forte ferramenta da análise diagnóstico de um modelo, no trabalho detectou-se que a análise clássica de resíduos sobre o ajuste do modelo de regressão linear robusta, assim como a análise das ponderações das observações, são medidas de detecção de pontos aberrantes eficientes. Foram aplicados os critérios e medidas analisados ao conjunto de dados obtido da Estação Meteorológica do Instituto de Astronomia, Geofísica e Ciências Atmosféricas da Universidade de São Paulo para detectar quais variáveis meteorológicas influem na temperatura mínima diária durante o ano completo, e ajustou-se um modelo que permite identificar os dias associados à entrada de sistemas frontais. / Robust Regression arises as an alternative to least squares method when errors are contaminated by outliers points or there are some evidence of violation of model assumptions. In classical regression there are several criteria for model selection and diagnostic measures that are well known. The objective of this work is to present the main robust criteria of model selection and outliers detection measures, as well as to analyze and compare their performance according to different stages to determine which of them fit better in certain situations. The cross-validation criteria using Monte Carlo simulations and Beyesian Information Criterion are known to be adequately developed in model identification. This fact was confirmed, and in addition, its robust alternatives also stand out in this aspect. The residual analysis is a strong tool for model diagnostic analysis, in this work it was detected that the classic residual analysis on the robust linear model regression fit, as well as the analysis of the observations weights, are efficient measures of outliers detection points. The analyzed criteria and measures were applied to the data set obtained from the Meteorological Station of the Astronomy, Geophysics and Atmospheric Sciences Institute of São Paulo University to detect which meteorological variables influence the daily minimum temperature during the whole year, and was fitted a model that allows identify the days associated with the entry of frontal systems.

[pt] Este trabalho tem como objetivo avaliar o uso da técnica Análise Condicionada da Demanda, que é uma metodologia que quebra o consumo de energia elétrica (neste trabalho do setor residencial) em suas partes por equipamento e por uso final, via Regressão Robusta em contrapartida à utilização da regressão clássica, na estimação do consumo de energia elétrica por uso final do setor residencial. Para isto foram realizadas análises via regressão linear múltipla e também análises via regressão robusta (estimadores robustos). Serão realizadas as duas análises para efeito de comparação entre o método clássico MQO - Mínimos Quadrados Ordinários, que não é o ideal, pois os dados violam os pressupostos para utilização desta técnica, e o método robusto, menos sensível a desvios de pressupostos / [en] This work has the purpose of evaluating the use of the technique Conditional Demand Analysis - CDA, which is a methodology that segregates the consumption of electric energy (on this work about the residential sector) is its parts per equipment and per final use through the Robust Regression, in counterpart of using the classic regression, in the estimation of the electric energy consumption for final use on the residential sector. For this purpose analyses will be made using the multiple linear regression and also analyses using the robust regression (robust estimators). The two analyses will be made for comparing the classic method Squared Minimums Usual - MQO, which is not the ideal one because the data violates the requirements for using this kind of method, and the robust method, less sensible to detours of the requirements.

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The work in this dissertation comprises aeroservoelastic simulation development, two modal filter design case studies and theoretical improvement of the modal filter. The modal filter is made robust to sensor bias. Studies have shown that the states estimated by the modal filter can be integrated into active structural control. The integration of modal filters into aircraft structural control systems is explored. Modal filters require distributed sensing to achieve accurate modal coordinate estimates. Distributed sensing technology has progressed to the point, where it is being tested on aircraft such as Ikhana and the upcoming X-56A. Previously, the modal filter was criticized for requiring too many sensors. It was never assessed for its potential benefits in aircraft control. Therefore it is of practical interest to reinvestigate the modal filter. The first case study shows that under conditions of sensor normality, the modal filter is a Gaussian efficient estimator in an aeroservoelastic environment. This is a fundamental experiment considering the fact that the modal filter has never been tested in the airflow. To perform this case study a linear aeroservoelastic code capable of modeling distributed sensing is developed and experimentally validated. From this code, a computational wing model is fitted with distributed sensing. A modal filtering design methodology is developed and applied. With distributed sensing and modal filtering feedback control is achieved. This is also compared and contrasted with a controller using state-of-the-art accelerometers. In addition, new methods of active shape control are introduced for warping an aeroelastic structure utilizing the modal filter and control surfaces. The next case study takes place in a realistic setting for an aircraft. Flexible aircraft bring challenges to the active control community. Increased gust loads, possibility of flutter, and off-design drag may detrimentally affect performance and safety. Aeroservoelastic tailoring, gust load alleviation (GLA) and active flutter suppression (AFS) may be required on future flexible air vehicles. It is found that modal filters can theoretically support these systems. The aircraft case study identifies additional steps required in the modal filtering design methodology. Distributed sensing, the modal filter and modal reference shape control are demonstrated on the X-56A flutter-unstable simulation model. It is shown that control of deformations at potentially millions of points on an aircraft vehicle can be achieved through control of a few modal coordinates. Finally modal filter robustness is theoretically improved and computationally verified. State-of-the-art modal filters have high bias sensitivity. In fact, this is so critical that state-of-the-art modal filters may never be certified for aircraft implementation. This is especially true within a flight critical control system. The solution to this problem is found through derivation of the robust modal filter. The filter combines good properties of concentration algorithms with robust re-descending M-estimation. A new trim criterion specific to the strain based modal sensing system is derived making the filter robust to asymmetric or leverage point outliers. Robust starts are introduced to improve convergence of the modal estimation system to the globally optimal solution in the presence of 100s of biased fiber optic sensors.

Capable and well-organized data mining algorithms are essential and fundamental to helpful, useful, and successful knowledge discovery in databases. We discuss several data mining algorithms including genetic algorithms (GAs). In addition, we propose a modified multivariate Newton's method (NM) approach to data mining of technical data. Several strategies are employed to stabilize Newton's method to pathological function behavior. NM is compared to GAs and to the simplex evolutionary operation algorithm (EVOP). We find that GAs, NM, and EVOP all perform efficiently for well-behaved global optimization functions with NM providing an exponential improvement in convergence rate. For local optimization problems, we find that GAs and EVOP do not provide the desired convergence rate, accuracy, or precision compared to NM for technical data. We find that GAs are favored for their simplicity while NM would be favored for its performance.

Social media has provided a way for music artists to reach many people with their music, without having to rely on record labels to perform marketing tasks. Most previous research within the area has focused on how already established music artists can use social media as part of their marketing strategies and how digital technologies have transformed the music industry. This study focuses on how unsigned music artists’ followers and fans on social media have an impact on their music streaming numbers. The main research question of the study is: how does unsigned artists’ social media performance affect the number of streams of their music? To investigate this, a robust regression model was defined with the aim of predicting the number of artists’ music streams based on their social media data. The robust regression model showed that the social media variables did not have significant effects on the number of streams. Therefore, an analysis of each individual artist in the data was conducted. The results showed that the social media data in this study could not be used to explain changes in the number of streams for unsigned music artists. An analysis based on each individual artist and the content that each individual artist is posting on the different social media channels, is suggested instead. An information visualization tool was developed with the purpose of allowing analysts to get an overview of the social media data as well as allow analysts to look at each artist’s social media feeds to understand how artists’ social media activities affect their music streaming data. / Sociala medier har gjort det möjligt för musikartister att nå många människor med sin musik utan att behöva förlita sig på skivbolag. Tidigare forskning inom området har fokuserat på hur redan etablerade musikartister kan använda sociala medier som en del av sina marknadsstrategier och hur digital teknik har förändrat musikbranschen. Denna studie fokuserar på hur osignerade musikartisters antal anhängare och fans på sociala medier påverkar antalet streams av artisternas musik. Studiens huvudsakliga forskningsfråga är: Hur påverkar osignerade artisters prestationer på sociala medier antalet streams av deras musik? För att undersöka detta definierades en robust regressionsmodell i syfte att förutse antalet streams av artisternas musik baserat på deras sociala mediedata. Den robusta regressionsmodellen visade att socialamedievariablerna inte hade signifikanta effekter på antalet streams av artisternas musik. Därför genomfördes en analys av varje enskild artist i datan. Resultaten visade att sociala mediedatan i denna studie inte kunde användas för att förklara förändringar i antalet streams för osignerade musikartister. En analys baserad på varje enskild artist och innehållet som varje enskild artist lägger ut på de olika sociala mediekanalerna föreslås istället. Ett informationsvisualiseringsverktyg utvecklades med syftet att ge analytiker en möjlighet att få en överblick över sociala mediedatan samt låta analytiker titta på varje artists sociala medieflöden för att förstå hur artisternas sociala medier påverkar deras musikstreamingdata.

本文探討退休金發展程度與投資策略對股票市場發展的影響，並同時採用「縱橫門檻迴歸模型」（panel threshold model, PTM）及結合縱橫門檻模型與穩健迴歸的「穩健縱橫門檻迴歸模型」（robust panel threshold model, ROPTM）來研究此一議題。我們用退休基金投資證券市場的金額佔總額的比例為分類標準，將樣本分為高投資比例與低投資比例兩部分。對部分OECD國家及台灣的panel data分析後之結果顯示：在股票市場方面，若基金採高投資比例之投資策略，則退休金發展或投資股市比例越高，越能促進股市發展；採低投資比例策略的基金，對股市發展的影響並不顯著。 / This paper analyzes the impact of pension fund investment on securities markets using a panel threshold model (PTM) and a robust panel threshold model (ROPTM) which combines a panel threshold model with a robust regression model. We use panel data for some OECD countries and Taiwan to test the validity of our propositions. The data is divided into low and high investment regions based on the value of securities as a percentage of total financial assets of the pension fund. Our results are the following. In the high stock investment region, pension funds have a positive impact on stock markets. Whereas, in the low stock investment region, the positive impact seems to disappear.

This thesis deals with modern statistical approaches and their application aimed at robust methods and neural network modelling. Selected methods are analyzed and applied on frequent practical problems in czech industry and technology. Topics and methods are to be benificial in real applications compared to currently used classical methods. Applicability and effectivity of the algorithms is verified and demonstrated on real studies and problems in czech industrial and research bodies. The great and unexploited potential of modern theoretical and computational capacity and the potential of new approaces to statistical modelling and methods. A significant result of this thesis is also an environment for software application development for data analysis with own programming language DARWin (Data Analysis Robot for Windows) for implemenation of effective numerical algorithms for extaction information from data. The thesis should be an incentive for boarder use of robust and computationally intensive methods as neural networks for modelling processes, quality control and generally better understanding of nature.

在一般的研究過程中均可能有離群觀測值產生，只要有離群觀測值存在， 就可能對研究結果產生極重大的影響。在統計學上常用的參數估計式中， 有許多極易受離群觀測值影響。因此本研究採用模糊族群分析混合最大概 似估計演算法運用在參數估計上，以去除離群觀測值對分析結果的影響。 本研究主要針對相關係數與迴歸係數的估計進行探討，利用演算法中所求 得之隸屬度，計算穩健相關係數和穩健迴歸係數，以期能正確估計參數值 。