Rashid Tarik Recurrent Neural Network Model

ISBN 13: 9783659352041

Recurrent Neural Network Model

 
9783659352041: Recurrent Neural Network Model
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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.

Biografía del autor:

He received his Philosophy Doctorate Degree in Computer Science and Informatics, College of Engineering, Mathematical and Physical Sciences, University College Dublin (UCD), 2001-2006, Dublin-Ireland.

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Buchbeschreibung Zustand: New. Publisher/Verlag: LAP Lambert Academic Publishing | 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. | Format: Paperback | Language/Sprache: english | 245 gr | 220x150x9 mm | 172 pp. Bestandsnummer des Verkäufers K9783659352041

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Buchbeschreibung LAP Lambert Academic Publishing Apr 2013, 2013. Taschenbuch. Zustand: Neu. Neuware - 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. 172 pp. Englisch. Bestandsnummer des Verkäufers 9783659352041

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Buchbeschreibung LAP Lambert Academic Publishing Apr 2013, 2013. Taschenbuch. Zustand: Neu. Neuware - 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. 172 pp. Englisch. Bestandsnummer des Verkäufers 9783659352041

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Buchbeschreibung LAP Lambert Academic Publishing Apr 2013, 2013. Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Neuware - 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. 172 pp. Englisch. Bestandsnummer des Verkäufers 9783659352041

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Buchbeschreibung LAP Lambert Academic Publishing, United States, 2013. Paperback. Zustand: New. Language: English . Brand New Book. 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. Bestandsnummer des Verkäufers KNV9783659352041

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Buchbeschreibung LAP LAMBERT Academic Publishing. Paperback. Zustand: New. 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. Paperback. Bestandsnummer des Verkäufers 9783659352041

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