Study of non-homogeneous linear second-order discrete dynamical systems with uncertainties: solution and stability with applications

[EN] We study, from a probabilistic standpoint, a full randomization of nonhomogeneous second-order linear difference equations assuming that its data (initial conditions, coefficients, and forcing term) are random variables. Our analysis consists of computing the so-called first probability density...

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Detalles Bibliográficos
Autores: Cortés, J.-C.|||0000-0002-6528-2155, Sferle, Sorina Madalina|||0000-0002-2028-1191, Navarro-Quiles, Ana
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/220388
Acceso en línea:https://riunet.upv.es/handle/10251/220388
Access Level:acceso abierto
Palabra clave:Random second order linear difference equations
Probability density function
Random variable transformation technique
Random stability
Uncertainty quantification
Mathematical modeling in biomathematics
Descripción
Sumario:[EN] We study, from a probabilistic standpoint, a full randomization of nonhomogeneous second-order linear difference equations assuming that its data (initial conditions, coefficients, and forcing term) are random variables. Our analysis consists of computing the so-called first probability density function of the solution, which is a stochastic process, and then analyzing the stability of the solution assuming that all data have an arbitrary joint probability density function. To achieve these goals, we take extensive advantage of the so-called random variable transformation technique. The theoretical results extend their deterministic counterpart, and then, they have many applications in real-world problems where uncertainty plays a key role. Our findings are first illustrated by means of several numerical examples, where different simulations are carried out, and, second, by means of a model belonging to biomathematics.