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Wyświetlanie 1-3 z 3
Tytuł:
Systemic Evolutionary Algorithm inspired by the methods of quantum computing to improve the accuracy of the model on the neuronal motion the end of the robot arm PR–02
Autorzy:
Wołynka, Ł.
Tchórzewski, J.
Powiązania:
https://bibliotekanauki.pl/articles/97323.pdf
Data publikacji:
2016
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
Evolution algorithms
Quantum computing
Modeling systems
Robot PR–02
Artificial Neural Networks
Environment MATLAB and Simulink
Opis:
The article contains selected results of research on the design Systemic Evolutionary Algorithm inspired by quantum informatics methods and description how to implement it in Matlab language in order to use for improve parameters neural model on example robot robot PR–02 arm motion. Initial population was based on weights matrix of artificial neural network. Randomly selected population of individual chromosomes in both the initial and in the following parent population have been converted to binary values, and these to quantum values by using created for this purpose quatization() function. Quantum gene value was determined on the basis of stonger pure state represented by different chromosomes, to which dequantization() function was used. Selection of individuals was conducted based on the model of neural robot PR–02 motion implemented in Matlab language using calculationsNeuralNetworks() function.
Źródło:
Computer Applications in Electrical Engineering; 2016, 14; 297-312
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design and research on artificial neural networks as electrical power system development models based on IEEE RTS data
Autorzy:
Tchórzewski, J.
Pytel, M.
Powiązania:
https://bibliotekanauki.pl/articles/97683.pdf
Data publikacji:
2015
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
artificial neural network
IEEE RTS data
electrical power system
MATLAB and Simulink environment
testing sensitivity
simulation studies
Opis:
The paper presents selected results of research on the design of artificial neural networks and training them using the electrical power system development model (EPS or EP system) based on IEEE RTS 96 test data, i.a. creation of training and test files, development of architecture of the artificial neural network, selection of parameters of the network, selection of appropriate training and testing method, etc. As a result of the development and training an ANN, the following EP system development models were obtained, which were examined for sensitivity to changes of the number of hidden layers, number of neurons in a layer, activation function, training method, etc. Subsequently, simulation models for studying fitness of the obtained models to the real systems. Interesting results were obtained, e.g. the method of the neural modelling of the system, the optimal architecture of the ANN that is a model of the system, possibilities and directions to improve a neural model of the system, etc.
Źródło:
Computer Applications in Electrical Engineering; 2015, 13; 231-244
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Systemic Evolutionary Algorithm inspired by methods of Quantum computer sciences for the improvement of the accuracy of neural models in electrical engineering and electrical power engineering
Autorzy:
Tchórzewski, J.
Powiązania:
https://bibliotekanauki.pl/articles/97692.pdf
Data publikacji:
2016
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
Evolutionary Algorithms
Quantum computer science
Quantum mixed number
Systems modelling
Robot PR–02
Artificial Neural Networks
MATLAB and Simulink environment
Electric Power Exchange
Opis:
The work contains selected results of research on the application of quantum computer science to a systemic evolutionary algorithm for the purpose of improving accuracy of neural models in electrical engineering and electrical power engineering. Artificial neural networks are used in neural modeling, which networks are designed and taught models of systems using available numerical data. Parameters of neural networks, and especially, elements of weight matrices, biases as well as parameters of activation functions may be improved using evolutionary algorithms. It seems that applying solutions offered by quantum computer science to systemic evolutionary algorithm, and especially, as regards creation of quantum initial population, quantum crossover and mutation operators as well as selection, considerably improves the accuracy of modelling, which was verified in MATLAB and Simulink environment using selected examples such as RP–02 robot’s arm movement, the development of the Polish Electrical Power Exchange (polish: TGEE) system, etc.
Źródło:
Computer Applications in Electrical Engineering; 2016, 14; 280-296
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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