MATEC Web of Conferences
Volume 362, 2022XXII International Conference on Computational Mechanics and Modern Applied Software Systems (CMMASS 2021)
|Number of page(s)||6|
|Published online||14 September 2022|
Application of spike neural network for stabilizing pendulum in the nonlinear formulation
1 Kazan Federal University, 18, Kremlyovskaya st., Kazan, 420008, Russia
2 Goethe-Universität Frankfurt am Main, 6 (PEG Building), Theodor-W.-Adorno-Platz, Frankfurt am Main, 60323, Germany
* e-mail: email@example.com
The article describes the solution to the problem of stabilizing a nonlinear system using machine learning methods. Neural networks are one of the promising directions in this area. The article describes a model of spiking neural network, which differs from previous generations of networks by its similarity to biological neurons. A pendulum on an elastic foundation was chosen as a dynamic system for the study. The input layer of the neural network is the so-called sensory neuron, and information about the deviation of the pendulum from the equilibrium position was received on it. The Leaky Integrate-and-Fire model of the spiking neural network was used. The article shows the process of stabilization of a pendulum on an elastic foundation. The closed system was built and a method for a numerical solution was implemented. Two configurations of control functions have been considered. It is shown that the time required to bring the system into a steady equilibrium state depends on the choice of the control function.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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