scholarly journals On application of artificial neural networks for modeling of water consumption.

2021 ◽  
pp. 70-84
Author(s):  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Water Consumption ◽  
Water System ◽  
Water Runoff ◽  
Don River ◽  
Water Management Complex ◽  
Dynamic Stochastic ◽  
Artificial Neural

The article shows the capabilities of artificial neural networks built on radial basis functions for the study of water consumption by various branches of the Don River basin water system. The use of mathematical models in the form of a system of differentiated equations is hampered by the uncertainty of the coefficients in their right-hand sides, which describe the intensities of processes of different natures: precipitation, water consumption by various sectors of the water management complex, water runoff during snow melting, transpiration, infiltration, etc. As a rule, these parameters are random, and the mathematical models describing the water balance are stochastic. The use of neural networks is very fruitful here. Without going into the physical essence of the processes, they can be used to approximate and make reliable predictions, which is a prerequisite for the development of dynamic-stochastic concepts in the management of water resources.

Prediction of Internal Stability for Geogrid-Reinforced Segmental Walls

2010 ◽  
Vol 163-167 ◽  
pp. 1854-1857
Author(s):  
Anuar Kasa ◽  
Zamri Chik ◽  
Taha Mohd Raihan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Statistical Methods ◽  
Retaining Wall ◽  
Retaining Walls ◽  
Internal Stability ◽  
Residual Soil ◽  
Concrete Masonry ◽  
Artificial Neural

Prediction of internal stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.

MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

2021 ◽  
pp. 81-87
Author(s):  
А.В. Милов
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Mathematical Models ◽  
Technological Process ◽  
Artificial Neural ◽  
Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

Prediction of External Stability for Geogrid-Reinforced Segmental Walls

2011 ◽  
Vol 462-463 ◽  
pp. 1319-1324 ◽  
Author(s):  
Anuar Kasa ◽  
Zamri Chik ◽  
Taha Mohd Raihan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Statistical Methods ◽  
Retaining Wall ◽  
Retaining Walls ◽  
Residual Soil ◽  
External Stability ◽  
Concrete Masonry ◽  
Artificial Neural

Prediction of external stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.

scholarly journals Synthesis and Parameterization of Gas Sensor Models

2021 ◽  
Vol 25 (1) ◽  
pp. 138-161
Author(s):  
O. G. Bondar ◽  
E. O. Brezhneva ◽  
O. G. Dobroserdov ◽  
K. G. Andreev ◽  
N. V. Polyakov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Output Signal ◽  
Electrochemical Sensors ◽  
Training Data ◽  
Data Generation ◽  
Technical Documentation ◽  
Cross Sensitivity ◽  
Artificial Neural

Purpose of research: search and analysis of existing models of gas-sensitive sensors. Development of mathematical models of gas-sensitive sensors of various types (semiconductor, thermocatalytic, optical, electrochemical) for their subsequent use in the training of artificial neural networks (INS). Investigation of main physicochemical patterns underlying the principles of sensor operation, consideration of the influence of environmental factors and cross-sensitivity on the sensor output signal. Comparison of simulation results with actual characteristics produced by the sensor industry. The concept of creating mathematical models is described. Their parameterization, research and assessment of adequacy are carried out.Methods. Numerical methods, computer modeling methods, electrical circuit theory, the theory of chemosorption and heterogeneous catalysis, the Freundlich and Langmuir equations, the Buger-Lambert-Behr law, the foundations of electrochemistry were used in creating mathematical models. Standard deviation (MSE) and relative error were calculated to assess the adequacy of the models.Results. The concept of creating mathematical models of sensors based on physicochemical patterns is described. This concept allows the process of data generation for training artificial neural networks used in multi-component gas analyzers for the purpose of joint information processing to be automated. Models of semiconductor, thermocatalytic, optical and electrochemical sensors were obtained and upgraded, considering the influence of additional factors on the sensor signal. Parameterization and assessment of adequacy and extrapolation properties of models by graphical dependencies presented in technical documentation of sensors were carried out. Errors (relative and RMS) of discrepancy of real data and results of simulation of gas-sensitive sensors by basic parameters are determined. The standard error of reproduction of the main characteristics of the sensors did not exceed 0.5%.Conclusion. Multivariable mathematical models of gas-sensitive sensors are synthesized, considering the influence of main gas and external factors (pressure, temperature, humidity, cross-sensitivity) on the output signal and allowing to generate training data for sensors of various types.

Cognitive Science and Educational Administration

2020 ◽  
Author(s):  
Colin W. Evers ◽  
Gabriele Lakomski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Cognitive Science ◽  
Nature Of Science ◽  
Early Development ◽  
Cognitive Neuroscience ◽  
Educational Administration ◽  
Large Scale ◽  
Artificial Neural

The influence of cognitive science on educational administration has been patchy. It has varied over four main accounts of cognition, which are, in historical order: behaviorism, functionalism, artificial neural networks, and cognitive neuroscience. These developments, at least as they may have concerned educational administration, go from the late 1940s up to the present day. There also has been a corresponding sequence of developments in educational administration, mainly motivated by accounts of the nature of science. The goal of producing a science of educational administration was dominated by the construal of science as a positivist enterprise. For much of the field’s early development, from the 1950s to the early 1970s, varieties of behaviorism were central, with brief excursions into functionalism. When large-scale alternatives to behaviorism finally began to emerge, they were mostly alternatives to science, and thus failed to comport with much of cognitive science. However, the emergence of postpositivist accounts of science has created the possibility for studies in administrator cognition to be informed by developments in neuroscience. These developments initially included the study of artificial neural networks and more recently have involved biologically realistic mathematical models that reflect work in cognitive neuroscience.

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