Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities

The paradigm of stochastic antiresonance is considered for a class of nonlinear systems with sector bounded nonlinearities. Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by a wi...

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Main Authors: Adrian-Mihail Stoica, Isaac Yaesh
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/26/2/115
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author Adrian-Mihail Stoica
Isaac Yaesh
author_facet Adrian-Mihail Stoica
Isaac Yaesh
author_sort Adrian-Mihail Stoica
collection DOAJ
description The paradigm of stochastic antiresonance is considered for a class of nonlinear systems with sector bounded nonlinearities. Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by a wide neural network of a single hidden layer, such as the error equation of Hopfield networks with respect to equilibria or visuo-motor tasks. It is shown that driving such systems with a certain amount of state-multiplicative noise, one can stabilize noise-free unstable systems. Linear-Matrix-Inequality-based stabilization conditions are derived, utilizing a novel non-quadratic Lyapunov functional and a numerical example where state-multiplicative noise stabilizes a nonlinear system exhibiting chaotic behavior is demonstrated.
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spelling doaj.art-42c8c2978d1d4235afd00273d6111bf22024-02-23T15:15:38ZengMDPI AGEntropy1099-43002024-01-0126211510.3390/e26020115Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type NonlinearitiesAdrian-Mihail Stoica0Isaac Yaesh1Faculty of Aerospace Engineering, University Politehnica of Bucharest, 060042 Bucharest, RomaniaControl Department, Elbit Systems, Ramat-Hasharon 3100401, IsraelThe paradigm of stochastic antiresonance is considered for a class of nonlinear systems with sector bounded nonlinearities. Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by a wide neural network of a single hidden layer, such as the error equation of Hopfield networks with respect to equilibria or visuo-motor tasks. It is shown that driving such systems with a certain amount of state-multiplicative noise, one can stabilize noise-free unstable systems. Linear-Matrix-Inequality-based stabilization conditions are derived, utilizing a novel non-quadratic Lyapunov functional and a numerical example where state-multiplicative noise stabilizes a nonlinear system exhibiting chaotic behavior is demonstrated.https://www.mdpi.com/1099-4300/26/2/115stochastic antiresonancesector-bounded nonlinearitiesstochastic systems with state- dependent noisestability analysisinfinitesimal generator
spellingShingle Adrian-Mihail Stoica
Isaac Yaesh
Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
Entropy
stochastic antiresonance
sector-bounded nonlinearities
stochastic systems with state- dependent noise
stability analysis
infinitesimal generator
title Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
title_full Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
title_fullStr Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
title_full_unstemmed Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
title_short Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
title_sort stochastic antiresonance for systems with multiplicative noise and sector type nonlinearities
topic stochastic antiresonance
sector-bounded nonlinearities
stochastic systems with state- dependent noise
stability analysis
infinitesimal generator
url https://www.mdpi.com/1099-4300/26/2/115
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AT isaacyaesh stochasticantiresonanceforsystemswithmultiplicativenoiseandsectortypenonlinearities