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  4. Development of an artificial neural network with oscillatory neurons for recognition of spectral images

Development of an artificial neural network with oscillatory neurons for recognition of spectral images

SISN.
2020;
Volume 7
: pp. 16 - 23
https://doi.org/10.23939/sisn2020.07.016
Authors:
  1. Roman Peleshchak
  2. Vasyl Lytvyn
  3. Ivan Peleshchak
  4. Victoria Vysotska
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University, Lviv, Ukraine
3
Lviv Polytechnic National University
4
Lviv Polytechnic National University, Information Systems and Networks Department; Osnabrück University, Institute of Computer Science

This paper shows a new type of artificial neural network with dynamic oscillatory neurons that have natural frequencies. Artificial neural network in the mode of information resonance implements a new method of recognition of multispectral images. The constructed neural network will recognize the input spectral images with the amplitude of the non-stationary signal commensurate with the amplitude of the noise signal, due to the resonance effect in nonlinear oscillatory neurons. A computer experiment was performed to recognize multispectral images by a dynamic neural network based on the resonance effect.

neural network
multispectral images
information resonance

Wang L. (1994). Adaptive Fuzzy Systems and Control. Design and Stability Analysis. New Jersey: Prentice Hall.

Jang J., Sun C., Muzutani E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. IEEE Transactions on Automatic Control, 42(10), 1482 - 1484.
https://doi.org/10.1109/TAC.1997.633847

Abiyev R., Kaynak O. (2008). Fuzzy wavelet neural networks for identification and control of dynamic plants - A novel structure and a comparative study, IEEE Trans. On Industrial Electronics, 55 (8), 3133 - 3140.
https://doi.org/10.1109/TIE.2008.924018

Bodyanskiy Y., Pliss I., Vynokurova O. (2010). Hybrid wavelet-neuro-fuzzy system using adaptive Wneurons. Wissenschaftliche Berichte, FH Zittau/Goerlitz, 106, 301 - 308.

Vynokurova O. (2009). Hybrid adaptive neuro-fuzzy and wavelet-neuro-fizzy inferences systems of computational intelligence in signal processing tasks under high level noise. Adaptive automatic control systems, 15 (35), 113 - 120.

Kholmansky A. (2006). Simulation of brain physics. Quantum magic, 3(3), 3126-3155.

Smith K. (2005). Sensory Systems Biology, Moscow BINOM. Laboratory of Knowledge.

Hameroff S., Penrose R. (1994). Quantum coherence in microtubules: A neural basis for emergent consciousness? J. of Consciousness Studies, 1, 91-118.

Slyadnikov E. (2007). Physical model and associative memory of the dipole system of the cytoskeleton microtubule. Journal of Technical Physics, 7, 77 - 86.
https://doi.org/10.1134/S1063784207070110

Lytvyn V., Vysotska V., Peleshchak I., Rishnyak I., Peleshchak R. (2018). Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model. International Journal of Intelligent Systems and Applications(IJISA), 10(4), 8 - 17.
https://doi.org/10.5815/ijisa.2018.04.02