On a unique solution and stability analysis of a class of stochastic functional equations arising in learning theory
Abstract
Numerous computational and learning theory models have been studied using probabilistic functional equations. Especially in two-choice scenarios, the vast bulk of animal behavior research divides such situations into two different events. They split these actions into two possibilities according to the animals’ progress toward a particular decision. However, reward plays a crucial role in such experiments because, based on the selected side and the food placement, such scenarios may be classified into four distinct categories. This article aims to explore the animals’ reactions to such circumstances by presenting a generic stochastic functional equation. By using the well-known fixed point theory results, we examine the existence, uniqueness, and stability of solutions to the suggested functional equation. Moreover, an example is included to emphasize the significance of our findings.
Acknowledgements
The author would like to thank the anonymous referees for their valuable comments and suggestions, which helped to improve the paper.
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Urysohn and Hammerstein operators on Hölder spaces
- Almost *-η-Ricci solitons on Kenmotsu pseudo-Riemannian manifolds
- On geometric properties of certain subclasses of univalent functions defined by Noor integral operator
- On a unique solution and stability analysis of a class of stochastic functional equations arising in learning theory
- Weak solution of a Neumann boundary value problem with 𝑝(𝑥)-Laplacian-like operator
Artikel in diesem Heft
- Frontmatter
- Urysohn and Hammerstein operators on Hölder spaces
- Almost *-η-Ricci solitons on Kenmotsu pseudo-Riemannian manifolds
- On geometric properties of certain subclasses of univalent functions defined by Noor integral operator
- On a unique solution and stability analysis of a class of stochastic functional equations arising in learning theory
- Weak solution of a Neumann boundary value problem with 𝑝(𝑥)-Laplacian-like operator
