IEEE Trans Neural Netw Learn Syst. 2018 Apr;29(4):1082-1094. doi: 10.1109/TNNLS.2016.2645602. Epub 2017 Feb 6.
This paper studies the effects of uniform input noise and Gaussian input noise on the dual neural network-based WTA (DNN- WTA) model. We show that the state of the network (under either uniform input noise or Gaussian input noise) converges to one of the equilibrium points. We then derive a formula to check if the network produce correct outputs or not. Furthermore, for the uniformly distributed inputs, two lower bounds (one for each type of input noise) on the probability that the network produces the correct outputs are presented. Besides, when the minimum separation amongst inputs is given, we derive the condition for the network producing the correct outputs. Finally, experimental results are presented to verify our theoretical results. Since random drift in the comparators can be considered as input noise, our results can be applied to the random drift situation.
本文研究了均匀输入噪声和高斯输入噪声对基于双神经网络的 WTA（DNN-WTA）模型的影响。我们表明，在均匀输入噪声或高斯输入噪声下，网络的状态（under either uniform input noise or Gaussian input noise）收敛到平衡点之一。然后，我们推导出一个公式来检查网络是否产生正确的输出。此外，对于均匀分布的输入，我们给出了网络产生正确输出的概率的两个下界（每种输入噪声各一个）。此外，当输入之间的最小间隔给定时，我们推导出了网络产生正确输出的条件。最后，给出了实验结果来验证我们的理论结果。由于比较器中的随机漂移可以被视为输入噪声，因此我们的结果可以应用于随机漂移情况。
