Estimates of storage capacity in the q-state Potts-glass neural network
Estimates of storage capacity in the q-state Potts-glass neural network
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DOI：
10.1088/1751-8113/43/44/445001
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发表时间：
2010-11
期刊：
影响因子：
--
通讯作者：
Daxing Xiong;Hong Zhao
中科院分区：
文献类型：
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作者：
Daxing Xiong;Hong Zhao
我们研究了Q态Potts-Glass神经网络的绝对存储容量，它决定了真正可以检索到多少记忆模式。通过理论分析，结合局域场的分布特点，给出了估算存储容量的一般公式，并给出了Q=2和Q=3的动态仿真比较。与以前的理论相比，当Q=2时，我们的估计与模拟结果吻合得很好，而当Q=3时，我们给出了存储容量的下界。这一结果可能为神经网络的可能应用提供有用的信息。
We study the absolute storage capacity of the q-state Potts-glass neural network which determines how many memory patterns can be really retrieved. By using theoretical analysis combined with a characteristic of the distribution of local field, a general formula for estimating the storage capacity is proposed, and dynamical simulations for q = 2 and q = 3 are presented for comparison. Compared with the previous theory, it is found that in the case of q = 2 our estimate is in good agreement with the simulation result, while for the case of q = 3 it provides a lower boundary of the storage capacity instead. The result may provide useful information for possible applications of neural networks.