NEW Recurrent Neural Network Model by Paperback Book (English) Free
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| Preise | 2013 | 2014 | 2015 | 2016 |
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| Schnitt | Fr. 63.45 (€ 64.90)¹ | Fr. 68.96 (€ 70.54)¹ | Fr. 76.90 (€ 78.67)¹ | Fr. 83.57 (€ 85.48)¹ |
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Recurrent Neural Network Model
DE PB NWISBN: 9783659352041 bzw. 3659352047, in Deutsch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkosten nach: AUT.
Von Händler/Antiquariat, BuySomeBooks.
LAP LAMBERT Academic Publishing. Paperback. New. Paperback. 172 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Neural networks deviate from other models by their ability to map inputs to the outputs and build complex relationships among variables without specifying them explicitly. In this work we provide an extensive literature survey of the related problems and study several approaches, including conventional predictive methods. As a result of our analysis we propose two new methods, the multicontext recurrent networks and the hybrid networks, i. e. , the autoregressive multicontext recurrent neural networks. We consider them in context of the forecasting system design and development. We developed a system of adaptive and dynamic networks for predicting the daily peak load of electric energy. The system maps the exogenous and endogenous inputs to the endogenous outputs. It can be used for characterizing the seasonal and daily loads of electric power as well as unseasonably hot or cold days, holidays and other exceptional situations. We use an adaptive mechanism to train the network, deal with old data, and reduce the growth in energy load. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
Von Händler/Antiquariat, BuySomeBooks.
LAP LAMBERT Academic Publishing. Paperback. New. Paperback. 172 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Neural networks deviate from other models by their ability to map inputs to the outputs and build complex relationships among variables without specifying them explicitly. In this work we provide an extensive literature survey of the related problems and study several approaches, including conventional predictive methods. As a result of our analysis we propose two new methods, the multicontext recurrent networks and the hybrid networks, i. e. , the autoregressive multicontext recurrent neural networks. We consider them in context of the forecasting system design and development. We developed a system of adaptive and dynamic networks for predicting the daily peak load of electric energy. The system maps the exogenous and endogenous inputs to the endogenous outputs. It can be used for characterizing the seasonal and daily loads of electric power as well as unseasonably hot or cold days, holidays and other exceptional situations. We use an adaptive mechanism to train the network, deal with old data, and reduce the growth in energy load. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
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Recurrent Neural Network Model
DE NWISBN: 9783659352041 bzw. 3659352047, in Deutsch, LAP Lambert Academic Publishing, neu.
Lieferung aus: Schweiz, zzgl. Versandkosten, Versandfertig innert 6 - 9 Tagen.
Recurrent Neural Network Model, Neural networks deviate from other models by their ability to map inputs to the outputs and build complex relationships among variables without specifying them explicitly. In this work we provide an extensive literature survey of the related problems and study several approaches, including conventional predictive methods. As a result of our analysis we propose two new methods, the multi-context recurrent networks and the hybrid networks, i.e., the auto-regressive multi-context recurrent neural networks. We consider them in context of the forecasting system design and development. We developed a system of adaptive and dynamic networks for predicting the daily peak load of electric energy. The system maps the exogenous and endogenous inputs to the endogenous outputs. It can be used for characterizing the seasonal and daily loads of electric power as well as unseasonably hot or cold days, holidays and other exceptional situations. We use an adaptive mechanism to train the network, deal with old data, and reduce the growth in energy load.
Recurrent Neural Network Model, Neural networks deviate from other models by their ability to map inputs to the outputs and build complex relationships among variables without specifying them explicitly. In this work we provide an extensive literature survey of the related problems and study several approaches, including conventional predictive methods. As a result of our analysis we propose two new methods, the multi-context recurrent networks and the hybrid networks, i.e., the auto-regressive multi-context recurrent neural networks. We consider them in context of the forecasting system design and development. We developed a system of adaptive and dynamic networks for predicting the daily peak load of electric energy. The system maps the exogenous and endogenous inputs to the endogenous outputs. It can be used for characterizing the seasonal and daily loads of electric power as well as unseasonably hot or cold days, holidays and other exceptional situations. We use an adaptive mechanism to train the network, deal with old data, and reduce the growth in energy load.
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Recurrent Neural Network Model (2013)
EN PB USISBN: 9783659352041 bzw. 3659352047, in Englisch, 172 Seiten, LAP LAMBERT Academic Publishing, Taschenbuch, gebraucht.
Neu ab: $69.13 (15 Angebote)
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Lieferung aus: Vereinigte Staaten von Amerika, Usually ships in 1-2 business days.
Von Händler/Antiquariat, tabletopart.
Neural networks deviate from other models by their ability to map inputs to the outputs and build complex relationships among variables without specifying them explicitly. In this work we provide an extensive literature survey of the related problems and study several approaches, including conventional predictive methods. As a result of our analysis we propose two new methods, the multi–context recurrent networks and the hybrid networks, i.e., the auto–regressive multi–context recurrent neural networks. We consider them in context of the forecasting system design and development. We developed a system of adaptive and dynamic networks for predicting the daily peak load of electric energy. The system maps the exogenous and endogenous inputs to the endogenous outputs. It can be used for characterizing the seasonal and daily loads of electric power as well as unseasonably hot or cold days, holidays and other exceptional situations. We use an adaptive mechanism to train the network, deal with old data, and reduce the growth in energy load. Paperback, Label: LAP LAMBERT Academic Publishing, LAP LAMBERT Academic Publishing, Produktgruppe: Book, Publiziert: 2013-03-04, Freigegeben: 2013-03-04, Studio: LAP LAMBERT Academic Publishing, Verkaufsrang: 11924850.
Von Händler/Antiquariat, tabletopart.
Neural networks deviate from other models by their ability to map inputs to the outputs and build complex relationships among variables without specifying them explicitly. In this work we provide an extensive literature survey of the related problems and study several approaches, including conventional predictive methods. As a result of our analysis we propose two new methods, the multi–context recurrent networks and the hybrid networks, i.e., the auto–regressive multi–context recurrent neural networks. We consider them in context of the forecasting system design and development. We developed a system of adaptive and dynamic networks for predicting the daily peak load of electric energy. The system maps the exogenous and endogenous inputs to the endogenous outputs. It can be used for characterizing the seasonal and daily loads of electric power as well as unseasonably hot or cold days, holidays and other exceptional situations. We use an adaptive mechanism to train the network, deal with old data, and reduce the growth in energy load. Paperback, Label: LAP LAMBERT Academic Publishing, LAP LAMBERT Academic Publishing, Produktgruppe: Book, Publiziert: 2013-03-04, Freigegeben: 2013-03-04, Studio: LAP LAMBERT Academic Publishing, Verkaufsrang: 11924850.
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Recurrent Neural Network Model
DE PB NW RPISBN: 9783659352041 bzw. 3659352047, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, THE SAINT BOOKSTORE [51194787], Southport, United Kingdom.
BRAND NEW PRINT ON DEMAND., Recurrent Neural Network Model, Rashid Tarik.
BRAND NEW PRINT ON DEMAND., Recurrent Neural Network Model, Rashid Tarik.
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