Finite Automata with Imperfect Information as Classification Tools

Abstract

The concepts of finite automata with imperfect information and finite automata as classification tools are main objectives of this study. These concepts stemming form the theory of finite automata are introduced here to support original ideas for intelligent data processing. The new definition of finite automata with imperfect information generalizes classical definitions of finite automata (deterministic and nondeterministic) and fuzzy automata. It leads to models with different kinds of information imperfectness. The idea usage of using finite automata in classification problems stems from ways of accepting input data. The idea is powerful when tied to finite automata with imperfect information, which is outlined in a case study of classification of a certain type of time series. This case study identifies also some interesting directions of further development of the newly introduced concepts.