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2 Computer Applications in Electrical Engineering

2 2016

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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

Wołynka Ł., Tchórzewski J.

Computer Applications in Electrical Engineering

2016

Vol. 14

297--312

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.

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

Tchórzewski J.

Computer Applications in Electrical Engineering

2016

Vol. 14

280--296

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.