198 research outputs found
Neural model with particle swarm optimization Kalman learning for forecasting in smart grids
No full textThis paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. © 2013 Alma Y. Alanis et al
A wind speed neural model with particle swarm optimization Kalman learning
No full textThis paper deals with a novel training algorithm for a neural network architecture for wind speed time series prediction. The proposed training algorithm is based in an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters The EKF-PSO based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed time series. The proposed model is trained and tested using real wind speed data values. In order to show the applicability of the proposed scheme Simulation results are included. © 2012 TSI Press
Real-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks
No full textEste artículo trata el problema de identificación de sistemas no lineales discretos usando redes neuronales recurrentes de alto orden entrenadas con un algoritmo basado en el filtro de Kalman extendido (EKF). El artículo también incluye el análisis de estabilidad para el sistema completo, en las bases de la técnica de Lyapunov. La aplicabilidad del esquema se ilustra a través de la implementación en tiempo real para un motor de inducción trifísico
Real-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks
No full textEste artículo trata el problema de identificación de sistemas no lineales discretos usando redes neuronales recurrentes de alto orden entrenadas con un algoritmo basado en el filtro de Kalman extendido (EKF). El artículo también incluye el análisis de estabilidad para el sistema completo, en las bases de la técnica de Lyapunov. La aplicabilidad del esquema se ilustra a través de la implementación en tiempo real para un motor de inducción trifásico
Neural modeling of the blood glucose level for type 1 diabetes mellitus patients
No full textThis paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. " 2013 Alma Y. Alanis et al.",,,,,,"10.1155/2013/197690",,,"http://hdl.handle.net/20.500.12104/43068","http://www.scopus.com/inward/record.url?eid=2-s2.0-84879290075&partnerID=40&md5=cc3ccfb7130389dad494823eb1611e09",,,,,,,,"Mathematical Problems in Engineering",,,,"2013",,"Scopu
Inverse optimal control with speed gradient for a power electric system using a neural reduced model
No full textThis paper presented an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems in the presence of external disturbances and parameter uncertainties, for a power electric system with different types of faults in the transmission lines including load variations. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) based algorithm. It is well known that electric power grids are considered as complex systems due to their interconections and number of state variables; then, in this paper, a reduced neural model for synchronous machine is proposed for the stabilization of nine bus system in the presence of a fault in three different cases in the lines of transmission. � 2014 Alma Y. Alanis et al
Alma Alanis y el misterio de Atlantis. Proyecto de ilustración editorial.
No full textProducción de un proyecto real de ilustración editorial. Diseño de cubierta, imagen de portada a color y 13 ilustraciones interiores en blanco y negro. La estética estará enmarcada dentro de las directrices de la autora de la novela, es decir, similar al de la ilustración editorial y publicitaria de la década de 1950. La memoria documenta la elaboración de los elementos finales. En ella se incluyen elementos de la preproducción, como diseño de personajes, diseño de utilería y diseño de escenarios; así como abocetado a lápiz y entintado de las piezas.https://sciencevalue.udit.es/tfm_grafico/1021/thumbnail.jp
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