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    Wyświetlanie 1-4 z 4
    Tytuł:
    Neural modeling of the electric power stock market in usage of MATLAB and Simulink tools for the day ahead market data
    Autorzy:
    Ruciński, D.
    Tchórzewski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/94831.pdf
    Data publikacji:
    2016
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
    Tematy:
    neuronal modelling
    MATLAB
    Simulink environment
    simulation research
    artificial neural network
    Opis:
    The work contains selected results of the modelling of neural Electric Power Exchange (EPE) in Poland. For modelling EPE system, artificial neural network (ANN) was constructed. ANN was learned and tested using of the next day market data. Generated neural model was used for simulation tests and susceptibility tests. Suitable model was implemented in Simulink. As a result of simulation tests and susceptibility testing a lot of interesting research results were obtained.
    Źródło:
    Information Systems in Management; 2016, 5, 2; 215-226
    2084-5537
    2544-1728
    Pojawia się w:
    Information Systems in Management
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural model of human gait and its implementation in MATLAB and Simulink Environment using Deep Learning Toolbox
    Autorzy:
    Tchórzewski, Jerzy
    Wielgo, Arkadiusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2052427.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    artificial neural network
    deep learning toolbox
    humanoid robots
    MATLAB environment
    Simulink environment
    modeling of human walking motion
    Opis:
    The article presents selected results of research on the modeling of humanoid robots, including the results of neural modeling of human gait and its implementation in the environment MATLAB and Simulink with the use of Deep Learning Toolbox. The subject of the research was placed within the scope of the available literature on the subject. Then, appropriate research experiments on human movement along a given trajectory were developed. First, the method of measuring the parameters present in the experiment was established, i.e. input quantities (displacement of the left heel, displacement of the right heel) and output quantities (displacement of the measurement point of the human body in space). Then, research experiments were carried out, as a result of which numerical data were measured in order to use them for teaching and testing the Artificial Neural Network. The Perceptron Artificial Neural Network architecture was used to build a model of a neural human walk along a given trajectory. The obtained results were discussed and interpreted, drawing a number of important conclusions.
    Źródło:
    Studia Informatica : systems and information technology; 2021, 1-2(25); 39-65
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural model of the vehicle control system in a racing game. Part 1, Design and its implementation
    Autorzy:
    Tchórzewski, Jerzy
    Bolesta, Arkadiusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2175160.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Artificial Neural Networks
    Godot Engine
    MATLAB
    Simulink environment
    CLion IDE
    video games
    Opis:
    The publication consist of two parts. Part 1 contains the results of research on the design, learning and implementation of the Perceptron Artificial Neural Network as a model of neural control of car movement on the racetrack. This part 1 presents the results of studies, including review of the methods used in video racing games from the point of view of the selection of a method that can be used in the own research experiment, selection of the Artificial Neural Network architecture, its teaching method and parameters for the intended research experiment, selection of the data measurement method to be used in ANN training, as well as development design of a car game, its implementation and conducting simulation tests. In designing the game of vehicle traffic on the racetrack, among others, Godot Engine game engine and MATLAB and Simulink programming environment. The numerical data (14 input quantities and two output quantities) for ANN training were prepared with the use of semi-automatic measurement of the race track control points. Part 2 shows i.a. the results of the testing and simulation experiments that confirm the correct functioning of both the game and the model of the neural control system. There were also shown, among others, the possibility of continuing research in the field of increasing the flexibility of the racing game, in particular the flexibility of the vehicle traffic control system through the use of other artificial intelligence methods, such as ant algorithms or evolutionary algorithms.
    Źródło:
    Studia Informatica : systems and information technology; 2022, 1(26); 23--44
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural model of the vehicle control system in a racing game. Part 2, Research experiments
    Autorzy:
    Bolesta, Arkadiusz
    Tchórzewski, Jerzy
    Powiązania:
    https://bibliotekanauki.pl/articles/2175161.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Godot Engine
    MATLAB
    Simulink environment
    Neural control system
    Perceptron Artificial Neural Networks
    video games
    Opis:
    This article, which is a continuation of the article under the same main title and subtitle: part 1 Design and its implementation, includes the obtained results of research experiments with the use of a designed and implemented racing game. It uses a neural model of the vehicle motion control system on the racetrack in the form of a Perceptron Artificial Neural Network (ANN). In designing the movement of vehicles on the racetrack, the following were used, inter alia, Godot Engine and MATLAB and Simulink programming environment. The numerical data (14 input quantities and two output quantities) for ANN training were prepared with the use of semi-automatic measurement of the race track control points. This article shows, among others, the results of 10 selected research experiments, testing and simulation, confirming the correct functioning of both the computer game and the model of the neural control system. As a result of simulation tests, it turned out that the longest lap of the track in the conducted experiments lasted 4 minutes and 55 seconds, and the shortest - 10.47 seconds. In five minutes, the highest number of laps was 34, while the lowest numbers of laps were 1 and 5. In the course of the experiments it was noticed that under the same conditions the ANN learning outcomes are sometimes different.
    Źródło:
    Studia Informatica : systems and information technology; 2022, 1(26); 45--60
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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