A fast simulation model combining with artificial neural networks for fin-and-tube condenser

2002 ◽  
Vol 31 (7) ◽  
pp. 551-557 ◽  
Author(s):  
Guo-liang Ding ◽  
Chun-lu Zhang ◽  
Hao Liu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulation Model ◽  
Fast Simulation ◽  
Model Combining ◽  
Artificial Neural ◽  
Tube Condenser

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

scholarly journals Numerical Simulation for the Sound Absorption Properties of Ceramic Resonators

Fibers ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 77
Author(s):  
Giuseppe Ciaburro ◽  
Gino Iannace
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulation Model ◽  
Sound Absorption ◽  
Ceramic Materials ◽  
Acoustic Behavior ◽  
Absorption Properties ◽  
Acoustic Resonators ◽  
Absorption Performance ◽  
Artificial Neural

This work reports the results of experimental measurements of the sound absorption coefficient of ceramic materials using the principle of acoustic resonators. Subsequently, the values obtained from the measurements were used to train a simulation model of the acoustic behavior of the analyzed material based on artificial neural networks. The possible applications of sound-absorbing materials made with ceramic can derive from aesthetic or architectural needs or from functional needs, as ceramic is a fireproof material resistant to high temperatures. The results returned by the simulation model based on the artificial neural networks algorithm are particularly significant. This result suggests the adoption of this technology to find the finest possible configuration that allows the best sound absorption performance of the material.

scholarly journals SIMULATION OF THE BEHAVIOR OF A COMPUTER SYSTEM USING ARTIFICIAL NEURAL NETWORKS

T-Comm ◽  
2021 ◽  
Vol 15 (5) ◽  
pp. 29-37
Author(s):  
Oleg I. Sheluhin ◽  
◽  
Aleksey Yu. Sharikov ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulation Model ◽  
Computer System ◽  
Transition Probabilities ◽  
Central Element ◽  
Data Generation ◽  
Network Element ◽  
Generative Adversarial Network ◽  
Artificial Neural

The design and the implementation of the simulation model of a computer system (CS) using artificial neural networks (ANNs) are considered. The purpose of the implementation is to create the easy-to-learn and easy-to-implement simulation model that allows to simulate both normal and anomalous processes in computer systems. The developed simulation model is the software, which consists of various modules combined using principles of the client-server architecture, allowing to run the model in both centralized and distributed modes of operation. The model allows to simulate the behavior of the CS with various topologies: a star, a tree, and a combination of these topologies. The main elements of the model are implemented in the form of four modules that fulfill their specific roles: an agent that generates data; a passive network element that transmits data with possible delays and losses; an active network element that processes and transmits data arriving at it, and the core – the central element of the model that receives data and sends it to additional modules for analysis. The modularity provides a high potential for further modifications of the simulation model by adding new modules. Using the generative adversarial network-based data generation module in the model makes it possible to generate data required for modeling the behavior of the studied CS. Based on the calculation of the Euclidean distance between matrices of transition probabilities of initial and generated data, it is shown that processes generated using the developed simulation model have a similar behavior with real ones. The designed model can be used to study the work of a real CS, including the imitation of an anomalous behavior.

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