ºÝºÝߣshows by User: julianpucheta / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: julianpucheta / Fri, 27 Feb 2015 17:04:01 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: julianpucheta Presentacion limac-unc /slideshow/presentacion-limacunc/45242698 presentacion-limac-unc-150227170401-conversion-gate02
The MAIN CONTRIBUTION is an on-line heuristic law to set the training process and to modify the NN topology based on the Levenberg-Marquardt method. An Area Predictor Filter using nonlinear autoregressive model based on neural networks for time series forecasting is introduced. The core of the proposal is to analyze the roughness (long or short term stochastic dependence) of time series evaluated by the Hurst parameter (H). The proposed law adapts in real time the topology of the filter at each stage of time series, changing the number of pattern, the number of iterations and the input vector length. The main results show a good performance of the predictor, considering in particular to time series whose H parameter has a high roughness of signal, which is evaluated by HS and HA, respectively. These results encouraged to continue working on new adjustment algorithms for time series modeling natural phenomena.]]>
The MAIN CONTRIBUTION is an on-line heuristic law to set the training process and to modify the NN topology based on the Levenberg-Marquardt method. An Area Predictor Filter using nonlinear autoregressive model based on neural networks for time series forecasting is introduced. The core of the proposal is to analyze the roughness (long or short term stochastic dependence) of time series evaluated by the Hurst parameter (H). The proposed law adapts in real time the topology of the filter at each stage of time series, changing the number of pattern, the number of iterations and the input vector length. The main results show a good performance of the predictor, considering in particular to time series whose H parameter has a high roughness of signal, which is evaluated by HS and HA, respectively. These results encouraged to continue working on new adjustment algorithms for time series modeling natural phenomena.]]> Fri, 27 Feb 2015 17:04:01 GMT /slideshow/presentacion-limacunc/45242698 julianpucheta@slideshare.net(julianpucheta) Presentacion limac-unc julianpucheta The MAIN CONTRIBUTION is an on-line heuristic law to set the training process and to modify the NN topology based on the Levenberg-Marquardt method. An Area Predictor Filter using nonlinear autoregressive model based on neural networks for time series forecasting is introduced. The core of the proposal is to analyze the roughness (long or short term stochastic dependence) of time series evaluated by the Hurst parameter (H). The proposed law adapts in real time the topology of the filter at each stage of time series, changing the number of pattern, the number of iterations and the input vector length. The main results show a good performance of the predictor, considering in particular to time series whose H parameter has a high roughness of signal, which is evaluated by HS and HA, respectively. These results encouraged to continue working on new adjustment algorithms for time series modeling natural phenomena. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentacion-limac-unc-150227170401-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The MAIN CONTRIBUTION is an on-line heuristic law to set the training process and to modify the NN topology based on the Levenberg-Marquardt method. An Area Predictor Filter using nonlinear autoregressive model based on neural networks for time series forecasting is introduced. The core of the proposal is to analyze the roughness (long or short term stochastic dependence) of time series evaluated by the Hurst parameter (H). The proposed law adapts in real time the topology of the filter at each stage of time series, changing the number of pattern, the number of iterations and the input vector length. The main results show a good performance of the predictor, considering in particular to time series whose H parameter has a high roughness of signal, which is evaluated by HS and HA, respectively. These results encouraged to continue working on new adjustment algorithms for time series modeling natural phenomena.
Presentacion limac-unc from Pucheta Julian
]]> 181 1 https://cdn.slidesharecdn.com/ss_thumbnails/presentacion-limac-unc-150227170401-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-julianpucheta-48x48.jpg?cb=1523501143 You have a choice. You can throw in the towel, or you can use it to wipe the sweat off of your face. -Gatorade. No voy a la cancha, no veo tv, no voy al cine, no escucho radio, no me escapo los fines de semana, no me voy de vacaciones, y viajar... si, me gusta viajar en el E1 y bajarme en la esquina de casa, ése es mi mejor viaje del mundo....!