TMLNN: triple-valued or multiple-valued logic neural network

We discuss the problem of representing and processing triple-valued or multiple-valued logic knowledge using neural network. A novel neuron model, triple-valued or multiple-valued logic neuron (TMLN), is presented. Each TMLN can represent a triple-valued or multiple-valued logic rule by itself. We will show that there are two TMLNs: TMLN-AND (triple-valued or multiple-valued “logic AND”) neuron and TMLN-OR (triple-valued or multiple-valued “logic OR”) neuron. Two simplified TMLN models are also presented, and show that a multilayer neural network made up of triple-valued or multiple-valued logic neurons (TMLNN) can implement a triple-valued or multiple-valued logic inference system. The training algorithm for TMLNN is presented and can be shown to converge. In our model, triple-valued or multiple-valued logic rules can be extracted from TMLNN with ease. TMLNN can thus form a base for representing logic knowledge using neural network