Numerical Analysis of Bio-signal Using Generative Adversarial Networks
| Autor: | Hiroki Takada, Shota Yamamoto, Koki Nakane, Rentarou Ono, Masumi Takada |
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| Rok vydání: | 2020 |
| Předmět: |
Theoretical computer science
Degree (graph theory) Computer science Stochastic process 05 social sciences SIGNAL (programming language) 010501 environmental sciences 01 natural sciences Determinism Nonlinear system Similarity (network science) 0502 economics and business 050207 economics Randomness Generative grammar 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030601515 HCI (45) |
| DOI: | 10.1007/978-3-030-60152-2_44 |
| Popis: | In this decade, it is not necessary to have technical knowledge for the investment since the automatic algorithms to sell/buy investment destination have been developed with artificial intelligence (AI). In our previous study, measuring similarity (stationarity, fractality, and degree of determinism) of variations in the exchange rates to the pseudo-exchange rates generated by Generative Adversarial Networks (GANs), we compared Winner processes with the GANs. From the viewpoint of stationarity, the similarity of sequences in the pseudo exchange rates were higher than those generated by the Winner processes, and high scores in the similarity were resulted from both sequences in terms of degree of determinism. In this study, we have applied this AI system to numerical simulations of the stabilogram whose randomness is remarkably greater than that of the other bio-signal in accordance with the nonlinear analysis. |
| Databáze: | OpenAIRE |
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