Examinando por Materia "LAMDA"

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    Ítem
    Advanced fuzzy-logic-based context-driven control for HVAC management systems in buildings
    (Institute of Electrical and Electronics Engineers Inc., 2020-01-01) Morales Escobar L.; Aguilar J.; Garces-Jimenez A.; Gutierrez De Mesa J.A.; Gomez-Pulido J.M.; Morales Escobar L.; Aguilar J.; Garces-Jimenez A.; Gutierrez De Mesa J.A.; Gomez-Pulido J.M.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Control in HVAC (heating, ventilation and air-conditioning) systems of buildings is not trivial, and its design is considered challenging due to the complexity in the analysis of the dynamics of its nonlinear characteristics for the identification of its mathematical model. HVAC systems are complex since they consist of several elements, such as heat pumps, chillers, valves, heating/cooling coils, boilers, air-handling units, fans, liquid/air distribution systems, and thermal storage systems. This article proposes the application of LAMDA (learning algorithm for multivariable data analysis) for advanced control in HVAC systems for buildings. LAMDA addresses the control problem using a fuzzy classification approach without requiring a mathematical model of the plant/system. The method determines the degree of adequacy of a system for every class and subsequently determines its similarity degree, and it is used to identify the functional state or class of the system. Then, based on a novel inference method that has been added to LAMDA, a control action is computed that brings the system to a zero-error state. The LAMDA controller performance is analyzed via evaluation on a regulation problem of an HVAC system of a building, and it is compared with other similar approaches. According to the results, our method performs impressively in these systems, thereby leading to a trustable model for the implementation of improved building management systems. The LAMDA control performs very well for disturbances by proposing control actions that are not abrupt, and it outperforms the compared approaches. © 2013 IEEE.
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    Ítem
    An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA
    (Institute of Electrical and Electronics Engineers Inc., 2020-01-01) Morales, Luis; Aguilar, Jose; Morales, Luis; Aguilar, Jose; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Clustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable Data Analysis), whose most important features are: a) it is a non-iterative fuzzy algorithm that can work with online data streams, b) it does not require the number of clusters, c) it can generate new partitions with objects that do not have enough similarity with the preexisting clusters (incremental-learning). However, in some applications, the number of created partitions does not correspond with the number of desired clusters, which can be excessive or impractical for the expert. Therefore, our contribution is the formalization of an automatic merge technique to update the cluster partition performed by LAMDA to improve the quality of the clusters, and a new methodology to compute the Marginal Adequacy Degree that enhances the individual-cluster assignment. The proposal, called LAMDA-RD, is applied to several benchmarks, comparing the results against the original LAMDA and other clustering algorithms, to evaluate the performance based on different metrics. Finally, LAMDA-RD is validated in a real case study related to the identification of production states in a gas-lift well, with data stream. The results have shown that LAMDA-RD achieves a competitive performance with respect to the other well-known algorithms, especially in unbalanced benchmarks and benchmarks with an overlapping of around 9%. In these cases, our algorithm is the best, reaching a Rand Index (RI) >98%. Besides, it is consistently among the best for all metrics considered (Silhouette coefficient, modification of the Silhouette coefficient, WB-index, Performance Coefficient, among others) in all case studies analyzed in this paper. Finally, in the real case study, it is better in all the metrics.
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    Ítem
    Edificios inteligentes que manejan sus sistemas de climatización
    (Universidad EAFIT, 2020-12-01) Martinez Guerrero, Christian Alexander; Martinez-Guerrero, Christian Alexander; Morales Escobar, L; Aguilar, Jose; Garcés Jiménez, Alberto; Gutierrez De Mesa, José Antonio; Gómez Pulido, Jose Manuel; GIDITIC
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    Ítem
    LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning
    (World Scientific Publishing Co, 2020-01-01) Morales L.; Aguilar J.; Chávez D.; Isaza C.; Morales L.; Aguilar J.; Chávez D.; Isaza C.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    This paper proposes a new approach to improve the performance of Learning Algorithm for Multivariable Data Analysis (LAMDA). This algorithm can be used for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class, through the contributions of all its descriptors. LAMDA has the capability of creating new classes after the training stage. If an individual does not have enough similarity to the preexisting classes, it is evaluated with respect to a threshold called the Non-Informative Class (NIC), this being the novelty of the algorithm. However, LAMDA has problems making good classifications, either because the NIC is constant for all classes, or because the GAD calculation is unreliable. In this work, its efficiency is improved by two strategies, the first one, by the calculation of adaptable NICs for each class, which prevents that correctly classified individuals create new classes; and the second one, by computing the Higher Adequacy Degree (HAD), which grants more robustness to the algorithm. LAMDA-HAD is validated by applying it in different benchmarks and comparing it with LAMDA and other classifiers, through a statistical analysis to determinate the cases in which our algorithm presents a better performance. © 2019 World Scientific Publishing Company.