DocumentCode :
3369752
Title :
Research on Quantum Adaptive Resonance Theory Neural Network
Author :
Hou Xuan
Author_Institution :
Sch. of Electron. & Inf., Northwestern Polytech. Univ., Xian, China
Volume :
8
fYear :
2011
fDate :
12-14 Aug. 2011
Firstpage :
3885
Lastpage :
3888
Abstract :
The subject of the paper is Quantum Neural Network. On one hand, introducing quantum theory into the structure or training process of Classical Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Neural Network by the means of quoting the thought, concept and principles of quantum theory directly. In the paper, we summarize the basic model and learning algorithm of quantum neuron, propose the model and learning algorithm of Quantum Adaptive Resonance Theory Neural Network through introducing quantum computing into adaptive resonance theory, apply which in pattern recognition as well. In summary, Quantum Adaptive Resonance Theory Neural Network have an advantage over Classical Adaptive Resonance Theory Neural Network in the positive rate of clustering.
Keywords :
ART neural nets; generalisation (artificial intelligence); learning (artificial intelligence); quantum computing; quantum theory; adaptive resonance theory neural network; generalization; learning; pattern recognition; quantum neuron; quantum theory; training algorithm; Adaptation models; Adaptive systems; Artificial neural networks; Computational modeling; Neurons; Quantum computing; Quantum mechanics; Adaptive Resonance Theory; Classical Neural Network; Quantum Adaptive Resonance Theory Neural Network; Quantum Computing; Quantum Mechanics; Quantum Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-61284-087-1
Type :
conf
DOI :
10.1109/EMEIT.2011.6023908
Filename :
6023908
Link To Document :
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