A networked smart home system based on recurrent neural networks and reinforcement learning

With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor enviro...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhongwang Li, Bin Deng
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	smart home swarm intelligent decision-making recurrent neural network reinforcement learning Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168
Acceso en línea:	https://doaj.org/article/e988470e4170493cb3e42d0f895f32ce
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e988470e4170493cb3e42d0f895f32ce
record_format	dspace
spelling	oai:doaj.org-article:e988470e4170493cb3e42d0f895f32ce2021-11-17T14:21:59ZA networked smart home system based on recurrent neural networks and reinforcement learning2164-258310.1080/21642583.2021.2001769https://doaj.org/article/e988470e4170493cb3e42d0f895f32ce2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/21642583.2021.2001769https://doaj.org/toc/2164-2583With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor environment. By building a multi-level network model, an integrated model system from analysis, prediction to decision-making is formed. The swarm intelligent decision-making ability of the networked smart home system is realized by applying a recurrent neural network and a reinforcement learning method. Meanwhile, the indoor simulation environment is built, the indoor environment variables are simulated and the performance of the system is verified by the simulation environment. The simulation results show that the networked smart home system has advantages over the single smart home equipment in the performance of indoor comfort improvement.Zhongwang LiBin DengTaylor & Francis Grouparticlesmart homeswarm intelligent decision-makingrecurrent neural networkreinforcement learningControl engineering systems. Automatic machinery (General)TJ212-225Systems engineeringTA168ENSystems Science & Control Engineering, Vol 9, Iss 1, Pp 775-783 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart home swarm intelligent decision-making recurrent neural network reinforcement learning Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168
spellingShingle	smart home swarm intelligent decision-making recurrent neural network reinforcement learning Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168 Zhongwang Li Bin Deng A networked smart home system based on recurrent neural networks and reinforcement learning
description	With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor environment. By building a multi-level network model, an integrated model system from analysis, prediction to decision-making is formed. The swarm intelligent decision-making ability of the networked smart home system is realized by applying a recurrent neural network and a reinforcement learning method. Meanwhile, the indoor simulation environment is built, the indoor environment variables are simulated and the performance of the system is verified by the simulation environment. The simulation results show that the networked smart home system has advantages over the single smart home equipment in the performance of indoor comfort improvement.
format	article
author	Zhongwang Li Bin Deng
author_facet	Zhongwang Li Bin Deng
author_sort	Zhongwang Li
title	A networked smart home system based on recurrent neural networks and reinforcement learning
title_short	A networked smart home system based on recurrent neural networks and reinforcement learning
title_full	A networked smart home system based on recurrent neural networks and reinforcement learning
title_fullStr	A networked smart home system based on recurrent neural networks and reinforcement learning
title_full_unstemmed	A networked smart home system based on recurrent neural networks and reinforcement learning
title_sort	networked smart home system based on recurrent neural networks and reinforcement learning
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/e988470e4170493cb3e42d0f895f32ce
work_keys_str_mv	AT zhongwangli anetworkedsmarthomesystembasedonrecurrentneuralnetworksandreinforcementlearning AT bindeng anetworkedsmarthomesystembasedonrecurrentneuralnetworksandreinforcementlearning AT zhongwangli networkedsmarthomesystembasedonrecurrentneuralnetworksandreinforcementlearning AT bindeng networkedsmarthomesystembasedonrecurrentneuralnetworksandreinforcementlearning
_version_	1718425451514298368

A networked smart home system based on recurrent neural networks and reinforcement learning

Ejemplares similares