Abstract
Artificial Neural Networks (ANNs) are powerful computational modeling tools, however there are still some limitations in ANNs. In this paper, we construct a new artificial neural network, which based on MAS theory and quantum computing algorithm. All nodes in this new ANN are presented as quantum computational (QC) agents, and these QC agents have learning ability via implementing reinforcement learning algorithm. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show this method is effective.
Supported by Key Project of the Ministry of Education of China for Science and Technology Researchment(ID:206035).
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Meng, X., Wang, J., Pi, Y., Yuan, Q. (2007). A Novel ANN Model Based on Quantum Computational MAS Theory. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_4
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DOI: https://doi.org/10.1007/978-3-540-74769-7_4
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