Entity

Time filter

Source Type

Mérida, Mexico

Ricalde L.J.,UADY | Catzin G.A.,UADY | Alanis A.Y.,University of Guadalajara | Sanchez E.N.,CINVESTAV
IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIASG 2011: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid | Year: 2011

In this paper, a Higher Order Wavelet Neural Network (HOWNN) trained with an Extended Kalman Filter (EKF) is implemented to solve the wind forecasting problem. The Neural Network based scheme is composed of high order terms in the input layer, two hidden layers, one incorporating radial wavelets as activation functions and the other using classical logistic sigmoid, and an output layer with a linear activation function. A Kalman filter based algorithm is employed to update the synaptic weights of the wavelet network. The size of the regression vector is determined by means of the Lipschitz quotients method. 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. © 2011 IEEE. Source


Guillermo J.E.,CINVESTAV | Ricalde Castellanos L.J.,UADY | Sanchez E.N.,CINVESTAV | Alanis A.Y.,Col. Universitaria
Neurocomputing | Year: 2015

In general, heart medical diagnosis devices are reliable and efficient; however, they are only present in huge or modern hospitals. Heart murmurs are one of the typical heart problems. In this paper, we propose a radial wavelet neural network (RWNN) classifier for heart murmurs (pulmonary insufficiency and tricuspid insufficiency). The extended Kalman filter (EKF) is used as a learning algorithm for the RWNN. The network inputs are dimensional features, extracted from real cardiac cycles, and three classification outputs. Proposed model classification accuracy is compared with a multilayer perceptron trained with Levenberg-Marquardt training algorithm and with extreme learning machine one. The proposed model is trained and tested using real heart cycles in order to show the applicability of the proposed scheme. © 2015. Source


Rangel E.,University of Guadalajara | Alanis A.Y.,University of Guadalajara | Ricalde L.J.,UADY | Arana-Daniel N.,University of Guadalajara | Lopez-Franco C.,University of Guadalajara
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper deals with a novel training algorithm for a neural network architecture applied to solar radiation time series prediction. The proposed training algorithm is based in a novel bio-inspired aging model-particle swarm optimization (BAM-PSO). The BAM-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 efficiently the complex nature of the solar radiation time series. The proposed model is trained and tested using real data values for solar radiation. © Springer International Publishing Switzerland 2014. Source


Alanis A.Y.,University of Guadalajara | Sanchez E.N.,CINVESTAV | Hernandez-Gonzalez M.,CINVESTAV | Ricalde L.J.,UADY
IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIASG 2011: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid | Year: 2011

This paper focusses on a discrete-time reduced order neural observer applied to a Linear Induction Motor (LIM) model, whose model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. Simulation results are included in order to illustrate the applicability of the proposed scheme. © 2011 IEEE. Source


Alanis A.Y.,University of Guadalajara | Ricalde L.J.,UADY | Simetti C.,University of Genoa | Odone F.,University of Genoa
Mathematical Problems in Engineering | Year: 2013

This 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. Source

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