Automated Smart Home Controller Based on Adaptive Linear Neural Network

2019 ◽  
Author(s):  
Puji Catur Siswipraptini ◽  
Rosida Nur Aziza ◽  
Iriansyah BM Sangadji ◽  
Indrianto Indrianto ◽  
Riki RuliA. Siregar
Keyword(s):  
Neural Network ◽  
Smart Home ◽  
Linear Neural Network

scholarly journals IoT for smart home system

2021 ◽  
Vol 23 (2) ◽  
pp. 733
Author(s):  
Puji Catur Siswipraptini ◽  
Rosida Nur Aziza ◽  
Iriansyah Sangadji ◽  
Indrianto Indrianto ◽  
Riki Ruli A. Siregar ◽  
...  
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Smart Home ◽  
Solar Panel ◽  
Energy Sources ◽  
Neural Network Method ◽  
Smart Appliances ◽  
The Neural Network ◽  
Network Method ◽  
Linear Neural Network

<p>This paper examines the integration of smart home and solar panel system that is controlled and monitored using IoT (internet ofthings). To enable the smart home system to monitor the activity within the house and act according to the current conditions, it is equipped with several sensors, actuators and smart appliances. All of these devices have to be connected to a communication network, so they can communicate and provide services forthe smart home’s in habitants. The smart home system was first introduced to provide comfort and convenience, but later it should also address many other things, e.g. the importance of the efficient use of energy or electricity and hybrid use of energy sources. A solar panel is added to the smart home prototype and its addition is studied. Adaptive linear neural network is implemented in the prototype as an algorithm for predicting decisions based on the current conditions. The construction of the proposed integrated systemis carried out through several procedures, i.e. the implementation of the adaptive linear neural network (ADALINE) as the neural network method, the design of the prototype and the testing process. This prototype integrates functionalities of several household appliances into one application controlled by an Android-based framework.</p>

scholarly journals Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design

2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Fault Detection ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Optimum Experimental Design ◽  
The Neural Network ◽  
Adaptive Thresholds ◽  
Linear Neural Network

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.

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