Logical relations widely exist in human activities. Human use them for making judgement and decision according to various conditions, which are embodied in the form of \emph{if-then} rules. As an important kind of cognitive intelligence, it is prerequisite of representing and storing logical relations rightly into computer systems so as to make automatic judgement and decision, especially for high-risk domains like medical diagnosis. However, current numeric ANN (Artificial Neural Network) models are good at perceptual intelligence such as image recognition while they are not good at cognitive intelligence such as logical representation, blocking the further application of ANN. To solve it, researchers have tried to design logical ANN models to represent and store logical relations. Although there are some advances in this research area, recent works still have disadvantages because the structures of these logical ANN models still don't map more directly with logical relations which will cause the corresponding logical relations cannot be read out from their network structures. Therefore, in order to represent logical relations more clearly by the neural network structure and to read out logical relations from it, this paper proposes a novel logical ANN model by designing the new logical neurons and links in demand of logical representation. Compared with the recent works on logical ANN models, this logical ANN model has more clear corresponding with logical relations using the more direct mapping method herein, thus logical relations can be read out following the connection patterns of the network structure. Additionally, less neurons are used.
翻译:人类活动中广泛存在着逻辑关系。 人类根据各种条件使用它们来作出判断和决定,这些条件体现在 kemph{if-then} 规则中。 作为一种重要的认知智能,这是在计算机系统中正确代表并储存逻辑关系的先决条件,以便进行自动判断和决定,特别是医疗诊断等高风险领域。 然而,目前的数字神经网络(人造神经网络)模型在感知智能方面是良好的,例如图像识别,而它们并不善于认知智能,例如逻辑代表,从而阻碍ANN的进一步应用。为了解决这个问题,研究人员试图设计逻辑的ANN模型来代表并储存逻辑关系。 尽管这一研究领域有一些进步,但最近的工程仍然有缺点,因为这些逻辑ANN模型的结构仍然不能更直接地与逻辑关系挂钩,而这种逻辑关系无法从网络结构中解读出来。 因此,为了更清楚地反映逻辑关系,为了通过神经网络结构结构来更清晰地反映逻辑关系,本文提出一个新的逻辑ANNN模型, 并且用逻辑关系中更清晰的逻辑关系来设计新的逻辑关系,因此, 与逻辑网络关系中的逻辑关系更不那么, 逻辑关系与逻辑关系中的逻辑关系可以更清晰地加以理解。