  With computers to handle more and more complicated things in variable
environments, it becomes an urgent requirement that the artificial intelligence
has the ability of automatic judging and deciding according to numerous
specific conditions so as to deal with the complicated and variable cases. ANNs
inspired by brain is a good candidate. However, most of current numeric ANNs
are not good at representing logical relations because these models still try
to represent logical relations in the form of ratio based on functional
approximation. On the other hand, researchers have been trying to design novel
neural network models to make neural network model represent logical relations.
In this work, a novel neural network model specified for representing logical
relations is proposed and applied. New neurons and multiple kinds of links are
defined. Inhibitory links are introduced besides exciting links. Different from
current numeric ANNs, one end of an inhibitory link connects an exciting link
rather than a neuron. Inhibitory links inhibit the connected exciting links
conditionally to make this neural network model represent logical relations
correctly. This model can simulate the operations of Boolean logic gates, and
construct complex logical relations with the advantages of simpler neural
network structures than recent works in this area. This work provides some
ideas to make neural networks represent logical relations more directly and
efficiently, and the model could be used as the complement to current numeric
ANN to deal with logical issues and expand the application areas of ANN.
