A Novel Structure Design and Training Algorithm for Quantum Neural Network

• Autorzy: Sun J. , Zhang X. , Cao T. , Yang J. , Sun X. , Liao Q.

Warianty tytułu

PL

Nowa struktura i algorytm uczenia w kwantowej sieci neuronowej

• Języki publikacji: EN

Abstrakty

EN

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.

PL

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.

Słowa kluczowe

EN

quantum neural network; transfer function; multi-tan-sigmoid; training algorithm

PL

kwantowa sieć neuronowa; algorytm uczenia

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 2
• Strony: 315--320
• Opis fizyczny: Bibliogr. 7 poz., rys., wykr.

Twórcy

autor

Sun J.

autor

Zhang X.

autor

Cao T.

autor

Yang J.

autor

Sun X.

autor

Liao Q.

• Institute of Communications Engineering, Biaoyin 2, Yudao Street, Nanjing, China,

Bibliografia

• [1] Graupe D., Principles of Artificial Neural Networks. 2nd. River Edge, NJ, USA: World Scientific Publishing Co., Inc., (2007).
• [2] Purushothaman G., Karayiannis N. B., Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks, Neural Networks, IEEE Transactions on, 8(1997), No. 3, 679- 693.
• [3] He Z., Zhang H., Zhao J., et al., Classification of power quality disturbances using quantum neural network and DS evidence fusion, European Transactions on Electrical Power, (2011).
• [4] Shen C., Huang H., Hwang R., Ammonia identification using shear horizontal surface acoustic wave sensor and quantum neural network model, Sensors and Actuators A: Physical, 147(2008), No. 2, 464-469.
• [5] Huang C., Huang H., Chen Y., et al., An AI system for the decision to control parameters of TP film printing, Expert Systems with Applications, 36(2009), No. 5, 9580-9583.
• [6] Zue V., Seneff S., Glass J., Speech database development at MIT: Timit and beyond, Speech Communication, 9(1990), No. 4, 351-356.
• [7] Yuhuan Z., Xiongwei Z., Jinming W., et al., Research on speaker feature dimension reduction based on CCA and PCA, Wireless Communications and Signal Processing (WCSP), 2010 International Conference on, China.