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

Author

Wang, Guoyin ; Shi, Hongbao

Author_Institution

Dept. of Comput., Univ. of Posts & Telecommun., Chongqing, China

Volume

9

Issue

6

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

1099

Lastpage

1117

Abstract

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

Keywords

feedforward neural nets; inference mechanisms; knowledge acquisition; knowledge representation; learning (artificial intelligence); multivalued logic; artificial intelligence; inference system; knowledge representation; learning algorithm; multilayer neural network; multivalued logic neurons; rule extraction; triple-valued logic neurons; Artificial intelligence; Artificial neural networks; Biological neural networks; Expert systems; Fuzzy logic; Humans; Knowledge acquisition; Multivalued logic; Neural networks; Neurons;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.728355

Filename

728355