.png){kind=icon}
返回
PARAMETER ESTIMATION SPACE FOR UNKNOWN INTERNAL EVOLUTION ON IOT DOMOTIC SYSTEMS
.png){kind=icon}
OA
AI
DOI：10.1142/S0218348X20500668.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes the parameter estimation modeling concerning a domotic designer bot system with internet of things (IoT) assistance using the probabilistic operator based on the stochastic parameter estimation through the moments and the recursive conditions. Light, CCTV, presence, and temperature are IoT data monitored, shared, and accessed by the internet for a smart office designer performance that evolves based on historical web data. The relationship established by Wiener between covariance and variance found the parameter time evolution by observing through the time. The development is viewed in the visible results between non-recursive and recursive mathematical structures. In both cases, the convergence rate is based on probabilistic estimation, the functional error presents a high convergence rate which is viewed as an effect of the function of a density function. The estimate considered a non-invasive perspective, and it helps in different applications such as health diagnosis in humans and animals with internal problems, or systems which are unknown for internal evolution such as for IoT model adoption. Therefore, our objective is to propose a black box, inner approximation through the parameter estimation without a no invasive stochastic method based in Wiener approximation.
Keyword:
Pattern Recognition
IoT (Internet of Things)
Parameter Estimation
Identification of Variables
Recursive Filters
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
F
IF:
2.9
论文数:
2.8K
被引数:
5.6K
机构
引用论文
分享收藏Simulated and experimental study of hydraulic anti-lock braking system using sliding-mode PWM control.png)
MECHATRONICS
MECHATRONICSIF3.1
PREAI
分享收藏
查看更多内容