scholarly journals SIMULATION MODELING OF NEURAL CONTROL SYSTEM FOR SECTION OF MINE VENTILATION NETWORK

2014 ◽  
pp. 106-116
Author(s):  
Iryna Turchenko ◽  
Volodymyr Kochan ◽  
Anatoly Sachenko
Keyword(s):  
Neural Network ◽  
Simulation Modeling ◽  
Neural Control ◽  
Dynamic Models ◽  
Simulation Models ◽  
Mine Ventilation ◽  
Ventilation Network ◽  
Control Approach ◽  
Network Training ◽  
Good Potential

Static and dynamic simulation models of a section of a mine ventilation network in order to research a sequential neural control scheme of mine airflow are developed in this paper. The techniques of neural network training set creation for both simulation models, a structure of neural network and its training algorithm are described. The simulation modeling results using static and dynamic models have showed good potential capabilities of neural control approach.

scholarly journals NEURAL-NETWORK BASED METHOD OF CONTROL INFLUENCES FORMING IN COMPUTER SYSTEM CONTROLLING ENVIRONMENTAL PARAMETERS

2014 ◽  
pp. 58-69
Author(s):  
Iryna Turchenko
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Simulation Modeling ◽  
Control Parameter ◽  
Environmental Parameters ◽  
Mine Ventilation ◽  
Ventilation Network ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training

A simulation model of a section of mine ventilation network is considered in this paper. The simulation modeling of transient aerogasdynamic processes of methane concentration changing is fulfilled at applying position and exponential control influences. There is proposed a neural-based method of control influences forming by neural network training on the set of optimal control influences. There are defined a criterion and developed an algorithm of optimal control influences forming as a training set of neural network. The simulation modeling of applying of control influences formed by neural network is fulfilled and decreasing of control parameter in the section of mine ventilation network is estimated.

Dynamic Modeling on the Hybrid MCFC-Gas Turbine Bottoming Cycle

2003 ◽  
Author(s):  
Huisheng Zhang ◽  
Shilie Weng ◽  
Ming Su
Keyword(s):  
Fuel Cell ◽  
Power Plant ◽  
Gas Turbine ◽  
Simulation Modeling ◽  
Dynamic Models ◽  
Simulation Models ◽  
Fuel Cell Stack ◽  
Hybrid Power ◽  
Hybrid Power Plant ◽  
Micro Turbine

The intention of this paper is to present the dynamic models for the MCFC-gas turbine hybrid cycle. This paper analyzes the performance of various components in the hybrid power plant, such as compressor, turbine, recuperator, generator, fuel cell stack etc. The modular simulation models of these components are presented. Based on the dynamic simulation modeling principle, one bottoming hybrid MCFC-Micro turbine cycle was studied to carry out the simulation, the simulation result is reasonable.

scholarly journals NEURAL-BASED RECOGNITION OF MULTI-PARAMETER SENSOR SIGNAL DESCRIBED BY MATHEMATICAL MODEL

2014 ◽  
pp. 140-147
Author(s):  
Iryna Turchenko ◽  
Volodymyr Kochan ◽  
Anatoly Sachenko
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Simulation Modeling ◽  
General Structure ◽  
Signal Recognition ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Sensor Signal ◽  
Network Training ◽  
Network Usage

The possibility of artificial neural network usage for recognition of a signal of a multi-parameter sensor is described in this paper. The general structure of data acquisition channel with usage of neural networks as well as mathematical model of output signal of a multi-parameter sensor is studied in this article. The model of neural network, training algorithm and achieved results of simulation modeling of a multi-parameter sensor signal recognition using MATLAB software are presented at the end of this paper.

Process simulation modeling of phytophagans' influence on the yield of seed fruit crops

2019 ◽  
pp. 138-143
Author(s):  
V. A. Shishkin ◽  
E. P. Rybalkin ◽  
E. B. Balykina
Keyword(s):  
Process Simulation ◽  
Simulation Modeling ◽  
Software Package ◽  
Development Process ◽  
Simulation Models ◽  
Vegetation Period ◽  
Fruit Crops ◽  
Generational Change ◽  
Apple Trees ◽  
Nonlinear Characteristics

Simulation modeling of phytophagans’ influence on the yield of seed fruit crops, in particular apple trees, was carried out. By means of simulation models the importance of phytophagans’ influence at different stages of the vegetation period and the period of fruit ripening was revealed. The software package Matlab was used to build simulation models. As a result, simulation models with nonlinear characteristics were obtained, which maximally reflected the studied processes. The developed models imitate the process of phytophagans’ development. Generation change of pests and all stages of their development are simulated. Their respective numbers are recorded at each stage for all generations. The development process at each stage is modeled by separate subsystems of the simulation model. To simulate the development of one generation of pests, these subsystems are connected by external links. In addition, part of the relationships provides a simulation of generational change. There are a number of input parameters that allow to configure the simulation of the process of changing generations, taking into account the peculiarities of the development of various phytophagans.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Convolutional Neural Network Formulation to Compare 4-D Seismic and Reservoir Simulation Models

2021 ◽  
pp. 1-14
Author(s):  
Klaus Rollmann ◽  
Aurea Soriano-Vargas ◽  
Forlan Almeida ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Reservoir Simulation ◽  
Simulation Models

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

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