scholarly journals System Control Device Electronics Smart Home Using Neural Networks

2017 ◽  
Vol 2 (5) ◽  
pp. 35-37
Author(s):  
Safarul Ilham ◽  
Abdul Muin ◽  
Dedi Candro
Keyword(s):  
Neural Networks ◽  
Smart Home ◽  
Control Device ◽  
System Control

Model of a neural network adaptive system for a digital control loop of an electric drive

2021 ◽  
Author(s):  
O.V. Nepomnyashchiy ◽  
A.V. Tarasov ◽  
Yu.V. Krasnobaev ◽  
V.N. Khaidukova ◽  
D.O. Nepomnyashchiy
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Electric Drive ◽  
Electric Motor ◽  
Control Device ◽  
System Control ◽  
Electric Transport ◽  
Control Loop ◽  
Software Environment ◽  
Power System Control

The problem of increasing the efficiency of power units of autonomous electric transport vehicles is considered. The task of creating a promising power system control device has been singled out. It is determined that in creating such devices, significant results can be obtained by using an intelligent module in the control loop of the electric drive. Goal. It is necessary to develop a power plant model with intelligent control, allowing to obtain data sets about currents, voltages and engine speeds in different modes of operation. The architecture of an intelligent control device, a PID controller based on a neural network, has been proposed; it has been proposed to exclude rotor angular velocity sensors from the classical feedback loop. The type and architecture of the neural network is defined. In the software environment MatLab the model of neuroemulator of the engine for formation of a training sample of a neural network by a method of Levenberg – Marquardt is developed. The trained neural network is implemented in the developed model of the electric motor control loop. The results of simulation of the intelligent control device showed a good convergence of the output influences generated by the neuroemulator with the actual parameters of the electric motor.

scholarly journals Methodology for Ensuring a Comfortable Microclimate State in a Smart Home Using an Ensemble of Fuzzy Artificial Neural Networks

2021 ◽  
Vol 20 (6) ◽  
pp. 1418-1447
Author(s):  
Andrey Mozohin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Physical Environment ◽  
Air Conditioning ◽  
Smart Home ◽  
Practical Implementation ◽  
Climate Control ◽  
Artificial Neural ◽  
Hybrid Neural Networks

Analysis of the application of smart home technology indicates an insufficient level of controllability of its infrastructure, which leads to excessive consumption of energy and information resources. The problem of managing the digital infrastructure of human living space, is associated with a large number of highly specialized solutions for home automation, which complicate the management process. Smart home is considered as a set of independent cyber-physical devices aimed at achieving its goal. For coordinated work of cyber-physical devices it is proposed to provide their joint work through a single information center. Simulation of device operation modes in a digital environment preserves the resource of physical devices by making a virtual calculation for all possible variants of interaction of devices between themselves and the physical environment. A methodology for controlling the microclimate of a smart home using an ensemble of fuzzy artificial neural networks is developed, with the example of joint use of air conditioning, ventilation and heating. The neural network algorithm allows you to monitor the parameters of the physical environment, predict the modes of cyber-physical devices and generate control signals for each of them, ensuring the joint operation of devices with minimal resource consumption and information traffic. A variant of practical implementation of a smart home climate control system on the example of a multifunctional educational computer class is proposed. Hybrid neural networks of air conditioning, ventilation and heating systems were developed. The testing of the microclimate control system of a multifunctional university classroom using hybrid neural networks was carried out, a programmable logic controller of domestic production was used as a control device. The goal of management based on cooperating cyber-physical devices is to achieve a minimum of power and information traffic when they work together.

scholarly journals Indirect Recognition of Predefined Human Activities

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4829
Author(s):  
Ojan Majidzadeh Gorjani ◽  
Antonino Proto ◽  
Jan Vanus ◽  
Petr Bilik
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Data Collection ◽  
Human Activities ◽  
Air Conditioning ◽  
Smart Home ◽  
Home Automation ◽  
Specific Activities ◽  
Conditioning Control

The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

scholarly journals Smart Home Design for Disabled People based on Neural Networks

2014 ◽  
Vol 37 ◽  
pp. 117-126 ◽  
Author(s):  
Ali Hussein ◽  
Mehdi Adda ◽  
Mirna Atieh ◽  
Walid Fahs
Keyword(s):  
Neural Networks ◽  
Smart Home ◽  
Disabled People

scholarly journals Application of Neural Networks for Control of District Heating \ Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie

2010 ◽  
Vol 56 (3) ◽  
pp. 219-238 ◽  
Author(s):  
W.J. Chmielnicki
Keyword(s):  
Neural Networks ◽  
Residential Buildings ◽  
District Heating ◽  
Heating System ◽  
Hot Water ◽  
System Control ◽  
Heating Systems ◽  
Quality Of Control ◽  
Building Maintenance ◽  
Non Linear

Abstract The annual usage of heat for the demand of heating systems in municipal sector has been estimated as about 650PJ. It is mostly addressed for the demand of central heating systems and hot water consumption. The mode of adopted solutions concerning regulation and control, as well as energy management system, essentially influence its consumption. In the case of residential buildings, the costs of energy constitute the greatest share related to the total cost of building maintenance. Providing buildings with modern digital systems for control and regulation of heating installations is a basic condition enabling their rational usage. In currently employed solutions, algorithms PI or PID are usually applied. However, due to the non-linear properties of heating control systems, they do not secure proper quality. The sequences are often unstable and major control deviations occur. The application of neural networks is an alternative solution to those presently employed. They are especially recommended for adaptive control of non-stationary systems. Such cases occur in heating objects since they demonstrate non-linear properties with a great range of variability of parameters; this especially refers to district heating equipped with flux-through heat exchangers. In this paper, a compile model of heating system control aided by neural networks is presented. The results of the investigation clearly prove the usefulness of such solutions, cause the quality of control is much better than that one applied in traditional systems. Presently, works on the implementation of the proposed solutions are under way.

scholarly journals Smart Home System for Energy Saving using Genetic- Fuzzy-Neural Networks Approach

2016 ◽  
Vol 08 (1) ◽  
Keyword(s):  
Neural Networks ◽  
Energy Saving ◽  
Human Behavior ◽  
Smart Home ◽  
Cost Effective ◽  
Fuzzy Neural Networks ◽  
Behavior Patterns ◽  
Power Losses ◽  
Sustainable Improvement ◽  
Fuzzy Neural

Home energy saving is very important to realize sustainable improvement. This can be achieved by designing a smart home system that provides a productive and cost-effective environment through optimization of different factors that will be explained in this paper. In this paper, an adaptive smart home system for optimal utilization of power will be designed. The system is based on genetic-fuzzy-neural networks technique, which can capture a human behavior patterns and use it to predict the user's mood. This technique will improve the intelligence of the smart home control to minimize the power losses.

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