scholarly journals A thermal quantum classifier

2020 ◽  
Vol 20 (11&12) ◽  
pp. 969-986
Author(s):  
Ufuk Korkmaz ◽  
Deniz Turkpence ◽  
Tahir Cetin Akinci ◽  
Cetin Akinci ◽  
Serhat Seker
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Thermal Environment ◽  
Quantum Model ◽  
Central Importance ◽  
Quantum Devices ◽  
Superconducting Circuits ◽  
Weakly Coupled ◽  
The Mathematical Model ◽  
Thermal Steady State

We find that the additivity of quantum information channels enables one to introduce a quantum classifier or a quantum decision maker. Proper measurement and sensing of temperature are of central importance to the realization of nanoscale quantum devices. Minimal classifiers may constitute the basic units for the physical quantum neural networks. We introduce a binary temperature classifier quantum model that operates in a thermal environment. In the present study, first the mathematical model was introduced through a two-level quantum system weakly coupled to the thermal reservoirs and it was demonstrated that the model faithfully classifies the temperature information of the reservoirs in the thermal steady state limit. A physical model by superconducting circuits composed of transmon qubits was also suggested.

scholarly journals Solution of Fractional Order System of Bagley-Torvik Equation Using Evolutionary Computational Intelligence

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Ijaz Mansoor Qureshi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Mathematical Model ◽  
Fractional Order ◽  
Computational Intelligence ◽  
Pattern Search ◽  
Fractional Order System ◽  
Order System ◽  
Search Technique ◽  
The Mathematical Model

A stochastic technique has been developed for the solution of fractional order system represented by Bagley-Torvik equation. The mathematical model of the equation was developed with the help of feed-forward artificial neural networks. The training of the networks was made with evolutionary computational intelligence based on genetic algorithm hybrid with pattern search technique. Designed scheme was successfully applied to different forms of the equation. Results are compared with standard approximate analytic, stochastic numerical solvers and exact solutions.

scholarly journals Mathematical model for the evaluation of risk of emergency situations at a dangerous technical object based on artificial neural networks

2018 ◽  
Vol 44 ◽  
pp. 00069
Author(s):  
Nikolay Peganov ◽  
Aleksandr Tumanov ◽  
Vladimir Tumanov
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Risk Assessment ◽  
Artificial Neural Networks ◽  
Fire Risk ◽  
Management Decision ◽  
Technical Object ◽  
Emergency Situations ◽  
Artificial Neural ◽  
The Mathematical Model

In the work performed adaptation of artificial neural networks in modern security systems potentially dangerous technical objects — high-rise buildings as tools for assessing and forecasting in management decision. The study obtained the main scientific results: the mathematical model of risk assessment of man-made emergencies based on artificial neural networks; the mathematical model, adapted to the cumulative model of development technogene emergency-fire; provided risk assessment technique manmade emergencies based on artificial neural networks; represented private man-made fire risk assessment methodology using artificial neural networks.

scholarly journals An Optimistic Solver for the Mathematical Model of the Flow of Johnson Segalman Fluid on the Surface of an Infinitely Long Vertical Cylinder

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7798
Author(s):  
Naveed Ahmad Khan ◽  
Fahad Sameer Alshammari ◽  
Carlos Andrés Tavera Romero ◽  
Muhammad Sulaiman ◽  
Seyedali Mirjalili
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Velocity Profile ◽  
Film Flow ◽  
Vertical Cylinder ◽  
Thin Film Flow ◽  
Sqp Algorithm ◽  
The Mathematical Model

In this paper, a novel soft computing technique is designed to analyze the mathematical model of the steady thin film flow of Johnson–Segalman fluid on the surface of an infinitely long vertical cylinder used in the drainage system by using artificial neural networks (ANNs). The approximate series solutions are constructed by Legendre polynomials and a Legendre polynomial-based artificial neural networks architecture (LNN) to approximate solutions for drainage problems. The training of designed neurons in an LNN structure is carried out by a hybridizing generalized normal distribution optimization (GNDO) algorithm and sequential quadratic programming (SQP). To investigate the capabilities of the proposed LNN-GNDO-SQP algorithm, the effect of variations in various non-Newtonian parameters like Stokes number (St), Weissenberg number (We), slip parameters (a), and the ratio of viscosities (ϕ) on velocity profiles of the of steady thin film flow of non-Newtonian Johnson–Segalman fluid are investigated. The results establish that the velocity profile is directly affected by increasing Stokes and Weissenberg numbers while the ratio of viscosities and slip parameter inversely affects the fluid’s velocity profile. To validate the proposed technique’s efficiency, solutions and absolute errors are compared with reference solutions calculated by RK-4 (ode45) and the Genetic algorithm-Active set algorithm (GA-ASA). To study the stability, efficiency and accuracy of the LNN-GNDO-SQP algorithm, extensive graphical and statistical analyses are conducted based on absolute errors, mean, median, standard deviation, mean absolute deviation, Theil’s inequality coefficient (TIC), and error in Nash Sutcliffe efficiency (ENSE). Statistics of the performance indicators are approaching zero, which dictates the proposed algorithm’s worth and reliability.

scholarly journals Aplicación de la ingeniería kansei con redes neuronales

2015 ◽  
Vol 1 (1) ◽  
pp. 7
Author(s):  
Gerardo Avendaño Prieto ◽  
Héctor René Álvarez L.
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Factor Analysis ◽  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Kansei Engineering ◽  
Statistical Assumptions ◽  
The Mathematical Model

ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍALa Ingeniería Kansei, relaciona las emociones que sienten los consumidores con las características y propiedades que poseen los productos a través de la estimación de un modelo matemático, que permite establecer cuáles tienen mayor relación con las emociones, permitiendo al diseñador, incorporarlas con el fin de activar los factores que las intensifican y dar soluciones efectivas de diseño.Tradicionalmente, la formulación y estimación del modelo matemático, se hace través de un modelo de regresión múltiple (QT1) y análisis factorial; sin embargo, una de las desventajas es que está condicionado a cumplir los supuestos del modelo. En este trabajo, se muestra cómo las redes neuronales se pueden aplicar en los estudios de Ingeniería Kansei y nos  da resultados similares, permitiendo que se puedan utilizar cuando no se cumplen los supuestos estadísticos. ABSTRACT Kansei Engineering (emotions) relates that consumers fee/ with features and properties that have the products through the estimation of a mathematical model, which allows for properties which are high relative to the emotions, al/owing the designer incorporate these relations to activate factors which enhance the Kansei design and give effective solutions. Traditionally the development of the mathematical model and estimation is done through a multiple regression model (QT1) and factor analysis, one of the disadvantages of the estimation of this model is that is conditioned to meet the model assumptions. This paper shows how neural networks can be applied in studies of Kansei Engineering and gives similar results, allowing for use when no statistical assumptions are met.

scholarly journals Developing the Automatic Control System Based on Neural Controller

2015 ◽  
Vol 44 (3) ◽  
pp. 262-270 ◽  
Author(s):  
Jun Su ◽  
Markiyan Nakonechnyi ◽  
Orest Ivakhiv ◽  
Anatoliy Sachenko
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Input Circuit ◽  
Nonlinear Functions ◽  
Neural Controller ◽  
Discrete Transformation ◽  
Network Training ◽  
Simulation Results ◽  
The Mathematical Model

Mostly the dynamics of controlled objects is often described by nonlinear equalizations. Last years themethodology of neural networks is engaged into designing the systems controlling such objects, in particular due to theinfluence of nonlinearities can be taken into account by nonlinear functions of the activation. Such methodology brings someintelligence to the designed system.Authors proposed the purposeful procedure of forming the structure of the neural controller according the desired lawof the control using the discrete transformation of the motion equation. Requirements to the mathematical model of thereference and method of network training are determined, and the control quality is estimated at traditional passing thedisagreement error in the controller input and for the proposed new configuration of its input circuit, namely with separatedinputs. Simulation results confirmed providing the better quality of the system control.DOI: http://dx.doi.org/10.5755/j01.itc.44.3.7717

THE INFLUENCE OF FLEXIBILITY OF THE SUPPORT ON DYNAMIC BEHAVIOR OF A CRANE

2011 ◽  
Vol 21 (10) ◽  
pp. 2963-2974 ◽  
Author(s):  
ANDRZEJ URBAŚ ◽  
MAREK SZCZOTKA ◽  
STANISŁAW WOJCIECH
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Dynamic Behavior ◽  
Dynamic Optimization ◽  
Optimization Method ◽  
Numerical Calculations ◽  
Final Position ◽  
Joint Coordinates ◽  
The Mathematical Model

The problem of control of the motion of a crane is considered in the paper. The mathematical model of the system is formulated using joint coordinates and homogenous transformations. The dynamic optimization method is applied in order to find drive functions realizing the desired trajectory and stabilizing the final position of the load at the end of motion in spite of the flexibility of the support. The results of numerical calculations and possible applications of models developed using artificial neural networks are also presented.

scholarly journals Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5127
Author(s):  
Szymon Buchaniec ◽  
Marek Gnatowski ◽  
Grzegorz Brus
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Box Model ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
The Mathematical Model

One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternatively, the prediction can be made by an artificial neural network (black-box) based on trends found in past data. Both approaches have their advantages and disadvantages. Mathematical models were seen as more trustworthy as their prediction is based on the laws of physics expressed in the form of mathematical equations. However, the majority of existing mathematical models include different empirical parameters, and both approaches inherit inevitable experimental errors. Simultaneously, the approximation of neural networks can reproduce the solution exceptionally well if fed sufficient data. The difference is that an artificial neural network requires big data to build its accurate approximation, whereas a typical mathematical model needs several data points to estimate an empirical constant. Therefore, the common problem that developers meet is the inaccuracy of mathematical models and artificial neural networks. Another common challenge is the mathematical models’ computational complexity or lack of data for a sufficient precision of the artificial neural networks. Here we analyze a grey-box solution in which an artificial neural network predicts just a part of the mathematical model, and its weights are adjusted based on the mathematical model’s output using the evolutionary approach to avoid overfitting. The performance of the grey-box model is statistically compared to a Dense Neural Network on benchmarking functions. With the use of Shaffer procedure, it was shown that the grey-box approach performs exceptionally well when the overall complexity of a problem is properly distributed with the mathematical model and the Artificial Neural Network. The obtained calculation results indicate that such an approach could increase precision and limit the dataset required for learning. To show the applicability of the presented approach, it was employed in modeling of the electrochemical reaction in the Solid Oxide Fuel Cell’s anode. Implementation of a grey-box model improved the prediction in comparison to the typically used methodology.

scholarly journals Prediction of Solutions of Arithmetic and Logical Operations on the Basis of the Mathematical Model of Cognitive Digital Automata

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Truth Table ◽  
Alternative Solution ◽  
Training Procedure ◽  
Feed Forward ◽  
Logical Operations ◽  
Feed Forward Neural Networks ◽  
The Mathematical Model ◽  
Training Sets

An approach to the problem of solution prediction of arithmetic and logical operations on the basis of the mathematical model of cognitive digital automata (CDA) is proposed. A particular advantage of the proposed approach is that the training procedure can be performed on limited (minimum) training sets. Prediction or generation of solutions is performed on the basis of the mathematical model of CDA which is formed in the course of training. As a testbed for the approach, the modeling of an n-bit parallel adder was implemented. The mathematical model of the adder was formed, which made it possible to reproduce the entire truth table for the n-bit parallel adder. The results obtained could be useful as an alternative solution to a number of problems known for conventional feed-forward neural networks, e.g. on-the-fly learning and catastrophic forgetting.

The Ventilation and Thermal Performance of the Patio Space in Shanghai Shikumen House

2013 ◽  
Vol 742 ◽  
pp. 8-12
Author(s):  
Xiao Yu Luo ◽  
Jian Ge ◽  
Yi Cai
Keyword(s):  
Mathematical Model ◽  
Thermal Performance ◽  
Simulation Analysis ◽  
Thermal Environment ◽  
Three Dimensional ◽  
Simulation Software ◽  
Wind Environment ◽  
Residential Architecture ◽  
Dimensional Simulation ◽  
The Mathematical Model

Shanghai Shikumen house is a typical one of Chinese traditional residential architecture, which has a "two patios & one corridor" pattern adapting to the climate of Shanghai. In existing researches, most study of the thermal performance dwelling with courtyard is still qualitative. This research constructed the quantitative simulation of the " two patios & one corridor" pattern in Shanghai Shikumen house based on the mathematical model by the technical simulation software, and compared the above mode with the general courtyard house to test the advances of it. First of all, the research conduct a three-dimensional simulation analysis on the "two patios & one corridor" pattern and the general "Two patios" pattern. Then we compared the results of the wind environment of the two modes and found the "two patios & one corridor" mode has advances on ventilation. Secondly, we did thermal environment simulations of the rooms in the above two modes, and compared the analysis results of two modes. We found that the "two patios & one corridor" pattern did better in the room thermal. Finally, we hope this research can provide certain reference for the design of the sustainable residential architecture.

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