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Comparison of the Classical Algorithm with the Training Algorithm in Scheduling Problem ADI Production

Wilk-Kołodziejczyk D., Chrzan K., Jaśkowiec K., Pirowski Z., Żuczek R., Bitka A., Machulec D.

Archives of Foundry Engineering

2022

Vol. 22, iss. 1

5--12

A classical algorithm Tabu Search was compared with Q Learning (named learning) with regards to the scheduling problems in the Austempered Ductile Iron (ADI) manufacturing process. The first part comprised of a review of the literature concerning scheduling problems, machine learning and the ADI manufacturing process. Based on this, a simplified scheme of ADI production line was created, which a scheduling problem was described for. Moreover, a classic and training algorithm that is best suited to solve this scheduling problem was selected. In the second part, was made an implementation of chosen algorithms in Python programming language and the results were discussed. The most optimal algorithm to solve this problem was identified. In the end, all tests and their results for this project were presented.

A Novel Structure Design and Training Algorithm for Quantum Neural Network

Sun J., Zhang X., Cao T., Yang J., Sun X., Liao Q.

Przegląd Elektrotechniczny

2012

R. 88, nr 2

315-320

In the structure of original Quantum Neural Network (QNN), only multi-sigmoid transfer function is adopted. Besides that, due to the conflict of the two objective functions in original training algorithm, the training process converges slowly and presents constant variation. In this paper, the QNN with multi-tan-sigmoid transfer function and a novel training algorithm which combines the two objective functions are proposed. Experimental results demonstrate the effectiveness of the structure improvement and the new training algorithm.

W oryginalnym algorytmie kwantowej sieci neuronowej QNN tylko multisigmoidalna funkcja przejścia jest wykorzystywana. W pracy zaprezentowano sieć z multi-tan-sigmoidalną funkcją przejścia z nowym algorytmem uczenia.