Open AccessA Pilot Study of Chaos Criteria with Hilbert Transform and Mutual Information
Author(s) -
Yahui Chen,
Yan Zhou,
Wenshi Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022058
Subject(s) - chaotic , mutual information , mathematics , chaos (operating system) , contrast (vision) , coding (social sciences) , pattern recognition (psychology) , feature (linguistics) , measure (data warehouse) , algorithm , computer science , artificial intelligence , statistics , data mining , linguistics , philosophy , computer security
Chaos feature criteria tree has many fruits with geometric maps and calculated values. To easily understand how to study chaos identification, we propose one new learning combination solution based on Hilbert transform (HT) and mutual information (MI). The most important coding probe ( x -axis) used the HT of locked uniformly distributed random number (LUSRN), so far the y -axis in contrast standard map points to LUDRN. The measure between contrast standard map and unknown map (normalized test data, HT of LUSRN) used MI values. The test cases cover five chaos equations and eighteen quasi-period functions. The results show in statistics that our new maps and corresponding MI values can classify three kinds of signals with periodic, chaotic and random states. And this work may promote rapid growth of an apple embedded in novel chaos criteria.