Strongly Unpredictable Oscillations of Hopfield-Type Neural Networks

Date

2020-10-01

Author

Akhmet, Marat
Nugayeva, Zakhira

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In this paper, unpredictable oscillations in Hopfield-type neural networks is under investigation. The motion strongly relates to Poincare chaos. Thus, the importance of the dynamics is indisputable for those problems of artificial intelligence, brain activity and robotics, which rely on chaos. Sufficient conditions for the existence and uniqueness of exponentially stable unpredictable solutions are determined. The oscillations continue the line of periodic and almost periodic motions, which already are verified as effective instruments of analysis and applications for image recognition, information processing and other areas of neuroscience. The concept of strongly unpredictable oscillations is a significant novelty of the present research, since the presence of chaos in each coordinate of the space state provides new opportunities in applications. Additionally to the theoretical analysis, we have provided strong simulation arguments, considering that all of the assumed conditions are fulfilled.

URI

https://hdl.handle.net/11511/68934

Journal

MATHEMATICS

DOI

https://doi.org/10.3390/math8101791

Collections

Department of Mathematics, Article

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Citation Formats

M. Akhmet and Z. Nugayeva, “Strongly Unpredictable Oscillations of Hopfield-Type Neural Networks,” MATHEMATICS, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68934.