Modeling and simulation of injection control system on a four-stroke type diesel engine development platform using artificial neural networks

2012 ◽  
Vol 22 (7-8) ◽  
pp. 1713-1725 ◽  
Author(s):  
Serhat Yilmaz ◽  
Mehmet Zeki Bilgin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Diesel Engine ◽  
Modeling And Simulation ◽  
Development Platform ◽  
Injection Control ◽  
Artificial Neural ◽  
Stroke Type ◽  
Engine Development

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.

Neural Controlling of Remotely Operated Underwater Vehicle

2011 ◽  
Vol 180 ◽  
pp. 168-174 ◽  
Author(s):  
Andrzej Żak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Control System ◽  
Underwater Vehicle ◽  
Mathematical Basis ◽  
Dynamical Object ◽  
Artificial Neural

The main aim of paper is to introduce the results of research concentrated on controlling remotely operated underwater vehicle using artificial neural networks. Firstly the mathematical basis of neural network using to control dynamical object were introduced. Next the proposed control system which is using technology of artificial neural network was presented. At the end the example results of research on stabilizing movements’ parameters of underwater vehicle using ROV simulator were presented. The paper is finished by summary which include conclusions derive from results of research.

pH-Control System Based on Artificial Neural Networks

1998 ◽  
Vol 37 (7) ◽  
pp. 2729-2740 ◽  
Author(s):  
María C. Palancar ◽  
José M. Aragón ◽  
José S. Torrecilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Ph Control ◽  
Artificial Neural

Artificial Neural Networks in Drug Transport Modeling and Simulation–I

2016 ◽  
pp. 221-241 ◽  
Author(s):  
Matthew MacPherson ◽  
Jeffrey Burgess ◽  
Brain McMillan ◽  
Todd Daviau ◽  
Srinivas M. Tipparaju
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Simulation ◽  
Drug Transport ◽  
Transport Modeling ◽  
Artificial Neural
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