Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3

Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Determination of constant-volume balloon capabilities for aeronautical research

The proper application of constant-volume balloons (CVB) for measurement of atmospheric phenomena was determined. And with the proper interpretation of the resulting data. A literature survey covering 176 references is included. the governing equations describing the three-dimensional motion of a CVB immersed in a flow field are developed. The flowfield model is periodic, three-dimensional, and nonhomogeneous, with mean translational motion. The balloon motion and flow field equations are cast into dimensionless form for greater generality, and certain significant dimensionless groups are identified. An alternate treatment of the balloon motion, based on first-order perturbation analysis, is also presented. A description of the digital computer program, BALLOON, used for numerically integrating the governing equations is provided

Magnetic calibration by using non-linear optimization method

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Monitoring for Disruptions in Financial Markets

Historical and sequential CUSUM change-point tests for strongly dependent nonlinear processes are studied. These tests are used to monitor the conditional variance of asset returns and to provide early information regarding instabilities or disruptions in financial risk. Data-driven monitoring schemes are investigated. Since the processes are strongly dependent several novel issues require special attention. One such issue is the sampling frequency. We study the power of detection as sampling frequencies vary. Analytical local power results are obtained for historical CUSUM tests and simulation evidence is presented for sequential tests. Finally, a prediction-based statistic is introduced that reduces the detection delay considerably. The prediction based formula is based on a local Brownian bridge approximation argument and provides an assessment of the likelihood of change-points. Nous étudions les tests CUSUM historiques et séquentiels pour des séries dépendantes avec des applications en finance. Pour les processus temporels, une nouvelle dimension se présente : l'effet du choix de la fréquence des observations. Un nouveau test est également proposé. Ce test est basé sur une formule de prévision locale d'un pont brownien.structural change, CUSUM, GARCH, quadratic variation, power variation, high frequency data, Brownian bridge, boundary crossing, sequential tests, local power, changement structurel, CUSUM, GARCH, variation quadratique, 'power variation', données de haute fréquence, pont Brownien, puissance locale, tests séquentiels

Latent growth mixture modeling: an application in the aeronautic training environment

The application of growth mixture modeling to longitudinal data offers an important extension of conventional modeling tools, enabling the identification of different patterns in growth, by accounting for population heterogeneity. The main goal of this study is to analyze the shape of the learning process in pilot training (Latent Growth Modeling), as well as to recognize different patterns in growth due to population heterogeneity (Growth Mixture Modeling). Moreover, the research intends to identify predictors that explain that variability and the pattern of growth. The object of study is the performance in flight training of ab-initio pilot applicants (n=297) to the Portuguese Air Force Academy (evaluated through six repeated measures). The results showed the existence of unobserved heterogeneity in the population and the best fitting model is a 2-class mixture model. Psychomotor coordination (SMA) showed a significant effect on the intercept (initial status) and the prognostic of General Adaptability (Personality and Motivation dimension) depicted a significant effect on the intercept (initial status) and on slope (development). The latent class 1 (66% of the sample) presents the highest initial flight performance, a positive significant effect of the General Adaptation on the intercept and the best results in the tests performed in the psychological phase. The latent class 2 (34% of the sample) presents the worst initial flight performance, and a positive significant effect of General Adaptation on the slope.A aplicação de modelo de mistura com crescimento latente a dados longitudinais oferece uma generalização importante dos modelos de crescimento convencionais, permitindo a identificação de diferentes padrões de crescimento, tendo em conta a heterogeneidade da população. O principal objectivo deste estudo consiste em analisar o processo de aprendizagem no treino de pilotos (modelos com crescimento latente), identificar diferentes padrões de crescimento resultantes da heterogeneidade existente (modelo de mistura com crescimento latente), e identificar as variáveis explicativas da variabilidade e do padrão de crescimento. O objecto de estudo é o desempenho no treino de pilotos ab-initio (n=297), candidatos à Academia da Força Aérea Portuguesa (avaliados em seis medidas repetidas). Os resultados obtidos demonstram que existe heterogeneidade não observada na população e que o modelo mais adequado é um modelo de mistura com crescimento latente de duas classes. A coordenação motora (SMA) demonstrou um efeito significativo no intercepto (estado inicial) e o prognóstico de Adaptabilidade Geral (dimensão Personalidade/Motivacional) demonstrou um efeito significativo quer no intercepto (estado inicial) quer no declive (aprendizagem). A classe latente 1 (66% da amostra) caracteriza-se por apresentar uma performance em voo superior no estado inicial (intercepto), um efeito significativo da Adaptabilidade Geral no intercepto, e melhores resultados nos testes realizados na fase de avaliação psicológica. Por sua vez, a classe latente 2 (34% da amostra) apresenta piores resultados relativos ao estado inicial da performance em voo, e um efeito significativo da Adaptabilidade Geral na aprendizagem (declive)