Modelling the hypnotic patient response in general anaesthesia using intelligent models

Jove, Esteban; González-Cava, José M.; Casteleiro‐Roca, José‐Luis; Méndez-Pérez, Juan-Albino; Reboso-Morales, José Antonio; Castelo, Francisco Javier Pérez; Juez, Francisco Javier de Cos; Calvo-Rolle, José Luís

doi:10.1093/jigpal/jzy032

Cited by 38 publications

(11 citation statements)

References 27 publications

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“…For the current prediction in this paper, several regression techniques had been checked. The algorithms based on multiple regression analysis are accepted regression methods used in several applications [32][33][34][35][36][37]. Some previous works have shown the use of these methods despite its low performance [33,[38][39][40].…”

Section: Fuel Outputmentioning

confidence: 99%

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

et al. 2019

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A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.

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Section: Fuel Outputmentioning

confidence: 99%

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

et al. 2019

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“…The rapid growth of available computational power and the development of new artificial intelligence methods has made possible the extensive use of these techniques for the prediction of both energy generation and consumption. Many examples of Artificial Intelligence (AI) based on predictive models can be found in the recent literature [11,12,13,14,15,16,17,18,19]. In [20], a model based on an Artificial Neural Network (ANN) to forecast building energy consumption is proposed.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling

Casteleiro‐Roca

Gómez‐González

Calvo-Rolle

et al. 2019

Sensors

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The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resorts.

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“…Hence, in renewable energy systems, or any industrial plant in general terms, the anomaly detection is a crucial task [6,7,8,9,10,11]. These anomalies can be produced by wrong sensor readings, actuator malfunctions or changes in plant parameters, in general terms [12,13,14,15,16,17,18]. Focusing on the sensors performance, the occasional reading errors can be removed and recovered, making the systems more fault tolerant and robust [19,20,21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Aláiz‐Moretón

Castejón-Limas

Casteleiro‐Roca

et al. 2019

Sensors

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This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.

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Modelling the hypnotic patient response in general anaesthesia using intelligent models

Cited by 38 publications

References 27 publications

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

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