Artículos (GIDITIC)

URI permanente para esta colección

https://hdl.handle.net/10784/7942

Examinar

Mostrando 1 - 4 de 4

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.
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.
Metropolis: Emergence in a Serious Game to Enhance the Participation in Smart City Urban Planning
(Springer Verlag, 2020-01-01) Aguilar J.; Díaz F.; Altamiranda J.; Cordero J.; Chavez D.; Gutierrez J.; Aguilar J.; Díaz F.; Altamiranda J.; Cordero J.; Chavez D.; Gutierrez J.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
This article presents a city simulator game named Metropolis. It is an emerging serious game that generates emergent properties. Metropolis can be used as a smart city for city planning, based on collective decisions. It also analyzes how its emergent properties might be used for managing a smart city and, especially, how it promotes e-participation as an e-decision-making tool within the context of urban planning. In addition, this paper explores the use of Metropolis for analyzing a smart city’s emergent citizen and urban patterns (urban spatial distribution) based on e-participation. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Modeling and control of nonlinear systems using an Adaptive LAMDA approach
(Elsevier BV, 2020-01-01) Morales L.; Aguilar J.; Rosales A.; Chávez D.; Leica P.; Morales L.; Aguilar J.; Rosales A.; Chávez D.; Leica P.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
This paper presents a soft computing technique for modeling and control of nonlinear systems using the online learning criteria. In order to obtain an accurate modeling, and therefore a controller with good performance, a method based on the fundamentals of the artificial intelligence algorithm, called LAMDA (Learning Algorithm for Multivariate Data Analysis), is proposed, with a modification of its structure and learning method that allows the creation of an adaptive approach. The novelty of this proposal is that for the first time LAMDA is used for fuzzy modeling and control of complex systems, which is a great advantage if the mathematical model is not available, partially known, or variable. The adaptive LAMDA consists of a training stage to establish initial parameters for the controller, and the application stage in which the control strategy is computed and updated using an online learning that evaluates the closed-loop system. We validate the method in several control tasks: (1) Regulation of mixing tank with variable dead-time (slow variable dynamics), (2) Regulation of a Heating, Ventilation and Air-Conditioning (HVAC) system (multivariable slow nonlinear dynamics), and (3) trajectory tracking of a mobile robot (multivariable fast nonlinear dynamics). The results of these experiments are analyzed and compared with other soft computing control techniques, demonstrating that the proposed method is able to perform an accurate control through the proposed learning technique. © 2020 Elsevier B.V.

Examinar

Examinando Artículos (GIDITIC) por Autor "Aguilar J."

Resultados por página

Opciones de ordenación