Abstract
This paper further studies the ability of the associate learning and self-correcting in a memristive artificial neural network (ANN). Different from the existing models, the present ANN contains the multiply-threshold neurons, the discrete charge-controlled memristors, and a new learning law named the max-input-feedback (MIF). We shall demonstrate the processes of the associative learning and associative correcting via a modified Pavlov experiment where more conditioning factors are considered. We also make some comparisons of MIF with spike-timing-dependent plasticity and back-propagation and show that MIF learning law is suitable to fast learning.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant 60974020, 60972155, 61101223, the Fundamental Research Funds for the Central Universities of China under Grant CDJXS10182215, CDJZR10185501, the Natural Science Foundation of Chongqing under Grant CSTC2009BB2305, and the Fundamental Research Funds for the Central Universities under Grant XDJK2010C023.
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Chen, L., Li, C., Wang, X. et al. Associate learning and correcting in a memristive neural network. Neural Comput & Applic 22, 1071–1076 (2013). https://doi.org/10.1007/s00521-012-0868-7
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DOI: https://doi.org/10.1007/s00521-012-0868-7