Abstract
We have proposed a hypothesis on the origin of randomness in the chaos time series of a chaos neural network (CNN) according to empirical results. An improved pseudo-random number generator (PRNG) has been proposed on the basis of the hypothesis and contamination mechanisms. PRNG has been implemented also with the fixed-point arithmetic (Q5.26). The result is expected to apply to embedded systems; for example the application of protecting personal information in smartphone and other mobile devices.
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Acknowledgements
The calculations in this study have performed with the SGI UV-100 and the GPGPU computers in Iwate University Super-Computing and Information Sciences Center (ISIC). Special thanks to Mr. Mitsuaki SASAKI for help with experiments.
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Yoshida, H., Murakami, T., Inao, T., Kawamura, S. (2016). Origin of Randomness on Chaos Neural Network. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_51
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