Chartier Sylvain, Proulx Robert
Université du Québec à Montréal, Montréal QC, H3C3P8, Canada.
IEEE Trans Neural Netw. 2005 Nov;16(6):1393-400. doi: 10.1109/TNN.2005.852861.
This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule. Several computer simulations show the model's distinguishing properties.
本文提出了一种新的无监督吸引子神经网络，与最优线性联想记忆模型不同，它能够产生非双极吸引子以及双极吸引子。此外，该模型能够产生更少的伪吸引子，并且在随机噪声下比任何其他霍普菲尔德型神经网络具有更好的召回性能。这些性能是通过一个简单的赫布/反赫布在线学习规则获得的，该规则直接纳入了来自特定非线性传输规则的反馈。几个计算机模拟展示了该模型的独特性质。
