IEEE Trans Cybern. 2021 Feb;51(2):686-695. doi: 10.1109/TCYB.2019.2913960. Epub 2021 Jan 15.
Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in a precise way using crisp numbers. In this paper, we present the long-term grey cognitive networks which expands the recently proposed long-term cognitive networks (LTCNs) with grey numbers. One advantage of our neural system is that it allows embedding knowledge into the network using weights and constricted neurons. In addition, we propose two procedures to construct the network in situations where only historical data are available, and a regularization method that is coupled with a nonsynaptic backpropagation algorithm. The results have shown that our proposal outperforms the LTCN model and other state-of-the-art methods in terms of accuracy.
通过神经网络模型来模拟真实世界的系统涉及许多挑战，包括从制定透明的知识表示到获得可靠的模拟误差。然而，这些知识往往难以用精确的方式用清晰的数字来表示。在本文中，我们提出了长期灰色认知网络，它扩展了最近提出的长期认知网络（LTCN）与灰色数字。我们的神经网络系统的一个优势是，它允许使用权重和约束神经元将知识嵌入到网络中。此外，我们提出了两种在只有历史数据可用的情况下构建网络的方法，以及一种与非突触反向传播算法相结合的正则化方法。结果表明，我们的方法在准确性方面优于 LTCN 模型和其他最先进的方法。
