State Complexity Approximation
Yuan Gao, Sheng Yu
TL;DR
This paper introduces the concept of state complexity approximation, a new method for estimating state complexities when exact values are unknown or too complex to understand.
Contribution
The paper develops the novel concept of state complexity approximation, enhancing the ability to estimate complexities in difficult or unknown cases.
Findings
Provides a new framework for state complexity estimation
Useful when exact complexities are unknown or incomprehensible
Enhances understanding of automata state complexities
Abstract
In this paper, we introduce the new concept of state complexity approximation, which is a further development of state complexity estimation. We show that this new concept is useful in both of the following two cases: the exact state complexities are not known and the state complexities have been obtained but are in incomprehensible form.
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Taxonomy
TopicsNeural Networks and Applications · Machine Learning and Algorithms · Fault Detection and Control Systems