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Análisis del caso Solución numérica de ecuaciones diferenciales con incertidumbre y aplicaciones

 <= /p>

Case analysis Numerical solution of differential equations with uncertainty and applications

 

 

Azorín Penalva Ainhoa. [1] & Jorge Luis Yaulema Castañeda.  [2]

 

 

Reci= bido: 25-06-2021 / Revisado: 02-07-2021 /Aceptado: 22-07-2021/ Publicado: 05-08-2= 021

 =

 

= Abstract.   =                         <= /span> DOI: hhttps://doi.org/10.33262/c= oncienciadigital.v4i3.1.1828  <= /span>

Introduc= tion: For the solution of problems that need to be solved exactly. whose solution must be treated by means of numerical methods Objective: The present investigation aims to carry out a study of the numerical methods of Euler a= nd Runge Kutta in order to make an approximation t= o the solution of random differential equations which use the calculus stochastic referring to the mean square. Methodology: Within the development process, Euler's methodology is analyzed in the first instance within the scalar case and later it is dimensioned to matrix problems, Results: obtaining an analy= sis of the application of numerical methods to the study of an electrical circu= it which is develops with random noise, in the specific case of characteristic= and irregular white noises they lead to other types of differential equations w= ith a certain degree of uncertainty called stochastic differential equations. Conclusion: The Euler scheme allows us to conclude that the slow convergence and the restriction aspect of its region of absolute stability allows us to consider other methods where the convergence is greater, thus proposing an additional study of the Runge-Kutta random sche= me, being a method superior to that of Euler for which its global order of convergence is fourth.

Keywords= : Euler, = Kutta Runge, numerical methods, differential equation= s

Resumen.=

Introducción: Para la solución de problemas= que requieren ser resueltos de manera exacta. cuya solución debe ser tratada por medio de los métodos numéricos  Objetivo: La presente investigación tiene como objetivo realizar un estudio de los métodos numéri= cos de Euler y Runge Kutta con la finalidad de real= izar una aproximación a la solución de ecuaciones diferenciales aleatorias las cuales utilizan el cálculo estocástico referente a la media cuadrática. = Metodología: Dentro del proceso de desarrollo la metodología de Euler se analiza en prim= era instancia dentro del caso escalar y posteriormente se lo dimensiona a probl= emas matriciales, Resultados: obteniendo un análisis de la aplicación de = los métodos numéricos al estudio de un circuito eléctrico el cual se desarrolla= con ruido aleatorio, en el caso específico de ruidos blanco característicos e irregulares conducen a otro tipo de ecuaciones diferenciales con cierto gra= do de incertidumbre denominadas ecuaciones diferenciales estocásticas. Conc= lusión: El esquema Euler permite llegar a la conclusión de que la convergencia lent= a y el aspecto de restricción de su región de estabilidad absoluta nos permite considerar otros métodos donde la convergencia es mayor, planteando así un estudio adicional del esquema aleatorio Runge- Kutta, siendo un método superior al de Euler por lo cual su orden de convergencia global es cuarto.

Palabras claves: Euler, Runge Kutta, métodos numéricos, ecuaciones diferenciales

=  

=  

Introducción.

Dentro de la aplicación de las matemáticas a situaciones del comportamiento diario y del mundo real nos encontramos a menudo con diferen= tes problemáticas, las cuales no pueden ser resueltas analíticamente o de manera exacta y cuya solución debe ser analizada mediante la resolución de problem= as a través de los métodos numéricos (Azor et al. 2020).

Bossis, et al (2015), manifiesta que en cuanto a = la planificación para la resolución de diferentes problemas como son el anális= is de un circuito eléctrico, tomando en cuenta las medidas referentes a la realidad tenemos la necesidad de planificar modelos aleatorios, de tal mane= ra que en la presente investigación se realizará la construcción numérica de soluciones para las ecuaciones diferenciales aleatorias a partir del esquema principal aleatorio de Euler mediante el cual se demostrará algunas condici= ones para que la convergencia en media cuadrática sea estable, además se calcula= rá el valor esperado y la varianza del proceso resultante de la aproximación <= /span>(Seminario 2012)

= Metodologia

La metodología utilizada en la presente investigación se basa en el análisis de métodos numéricos relacionados a la esquematización propuesta p= or Euler y Runge Kutta para la solución de ecuaciones diferenciales aleatorias de acuerdo al cálculo estocástico de la media cuadrática.

Método Numérico

El método numérico se establece por medio de un procedimiento que se basa en las aproximaciones para encontrar la solución a ciertos problemas u= tilizando cálculos puramente aritméticos y operaciones lógicas elementales. Hen= rici (1962), el procedimiento que consiste en una lista fi= nita de instrucciones que precisan una secuencia de operaciones algebraicas y lógicas que producen una aproximación de la solución a la problemática (Azor et al. 2020).

La eficiencia en el cálculo de dicha aproximación depende principalm= ente del escogimiento y facilidad de implementación del algoritmo y de las características especiales y limitaciones de los instrumentos de cálculo pudiendo generarse así un error de redondeo (Cortés, J= odar y Villafuerte, 2007).

Esquema de Método Euler para ecuaciones diferencias aleatorias<= /o:p>

El esquema de Euler, presenta una convergencia lenta = con una restricción de su región de estabilidad absoluta la cual considera otros métodos donde la convergencia puede ser mayor (Khodabi= n y Rostami, 2015 y Soong, 1973) . Tal es el caso del esquema aleatorio de RugeKutta, que representa un método de orden superior= que al de Euler, representado así por su orden de convergencia global cuarto (Azorín, 2020).

Esquema del Método Runge Kutta para ecua= ciones diferenciales aleatorias

Los métodos numéricos son metodologías que parten de técnicas algebraicas y aritméticas utilizadas para poder resolver de forma aproximada de diferentes ecuaciones o sistemas de ecuaciones muy complejas.= Las cuales analíticamente resultan muy dificultosos e incluso imposibles de obt= ener alguna solución (Villafuerte, 2007 y Aldana, et al 2016).

Puesto que, el proceso de convergencia lenta del méto= do de Euler y lo restringido de su región de estabilidad absoluta, establecen = la necesidad de métodos de orden de convergencia mayor (Oksendal, 1998).=

Además, en cada paso, el método de Euler, se mueve a = lo largo de la tangente de una cierta curva que esta “cerca” a la curva desconocida o buscada. Requiriendo por los mencionados motivos utilizar los= métodos Runge-Kutta que extienden esta idea geométrica utilizando varias derivadas o tangentes intermedias en lugar de solo una, p= ara aproximar la función desconocida (Lecca y Puent= e, 2006).

Resultados

Se muestran soluciones aproximadas de ciertas ecuacio= nes diferenciales con condiciones<= span style=3D'letter-spacing:-.55pt'> iniciales usando los métodos de Euler y Runge-Kutta al= eatorios, tratando inicialmente la determinación del desplazamiento por un movimiento terrestre.

1.1   Determinación del desplazamiento por un movimiento terrestre

Para determinar el efecto sobre una estructura terres= tre de una perturbación de tipo terremoto. Definiendo a la estructura en reposo= en , y sea , el desplazamiento (relativo) horizontal del techo c= on respecto al suelo. Basándose en un modelo lineal idealizado, el desplazamie= nto relativo  = se rige por la ecuación diferencial.

 


         =                                               (1.1)

 

 

Donde  = es el proceso estocástico de segundo orden dado por (1.1). La solución en media cua= drática tiene la forma:

 

                                                 =                                        (1.2)

 

con  < 1, y donde  = es la respuesta impulsiva

                                       =        

 

                                     (1.3)

 

<= span lang=3DEN-US style=3D'font-size:11.0pt;font-family:"Century",serif;mso-fare= ast-font-family: Century;mso-bidi-font-family:Century;position:relative;top:6.0pt;mso-text-r= aise: -6.0pt;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language: AR-SA'>                  (1.4)

 

Por lo tanto, de= las ecuaciones (1.2)-(1.4), se deduce <= /span>que

 

             (1.5)

 

Para aplicar el esquema aleator= io de Euler al problema, se convierte la ecuación diferencial de segundo orden= en un sistema de ecuaciones diferenciales de primer orden. Dejando  y = Xt= T=3D<= m:dPr>X1t= ,X2t, la forma de la matriz vectorial de la ecuación (1.1) es

 

Donde y se han obtenido en (1.15) y  = Como , la expresión del método aleatorio de Euler en este ca= so toma la forma.

=

 

Donde la matriz de identidad de tamaño 2, las hipótes= is H1 y H2 se cumplen, luego la convergencia en media cuadrática del método aleatorio de Euler está garantizada. De hecho, H1 se cumple, y se obtiene la condición de Lipschitz.

 

,

 

Considerando que , se obtiene que  = y , luego la matriz de covarianza del vector aleatorio = =  = será:

 

         =                                                                      (1.6)

 

Siendo

 

,

 

Donde  = está definido por (1.5). Teniendo en cuenta que si ,= denota la matriz cuadrada de tamaño 2 dada por (1.6), entonces es la varianza aproximada del proceso estocástico de segundo orden (1.1), obtenida del método aleatorio escalar de Euler.

1.= 2   Problema matricial lineal bilatera= l completo de Sylverter

Se considera el sistema diferencial aleatorio matrici= al no homogéneo dado por

 


,                                                 =                (1.7)

,

 

Donde

=

 

Siendo 0 la matriz nula de tamaño 2 × 2 y  un proceso de movimiento Browniano. Ya que y , entonces para  = resulta que

 

y por el Lema 1.0.1 se obtiene

 

||F(X, t) − F(X, t)||r×s ≤ ||A(X − Y)||r×s + ||(X − Y)B||r×s

 ≤ (||A||∞ + ||B||∞)||X − Y||r×s,                        (1.8)

=  

La ecua= ción del problema (1.7= ) satis= face las hipótesis  H1 y H2, por lo tanto, el método aleatorio de Euler es convergente en media cuadrática

Denotando por E[Xn] =3D (Eij )2×2 la espe= ranza de la matriz de tamaño 2x2 de las aproximaciones de Euler.

1.2.1 Pro= blema escalar lineal con termino fuente Browniano

 


,                                                 =              (1.9)

,

 

Donde B(t) es el proceso movimiento Browniano estándar y  es la variable aleatoria, ~  independiente del movimiento Browniano <= /span> para cada .

Figura 1

Valores= numéricos de de la matriz esperanza del Problema Matricial

 

 

 

 

 

 

 

 

 

 

 


Fuente: El Autor

Figura 2

Valores= numéricos de  <= span class=3DSpellE>de la matriz= esperanza del Problema Matricial

 

 

 

 

 

 

 

 

 

 

 

 


Fuente: El Autor

 

 

Se nota que  cum= ple la condición de Lipschitz H2= , ya que

 =

 =

Si  =3D , entonces

 

                                              (1.= 10)

                     =                       

y  esta acot= ada aleatoriamente, presentándose de man= era continua uniforme dentro de cualquier conjunto acotado . El  método aleatorio de cuarto orden de Runge-Kutta es:

                                                                        (1.11)

      

Dond= e         

 

1.3   El  <= span style=3D'mso-spacerun:yes'> método  aleatorio  <= /span>de  <= /span>Euler   y  su  convergencia  en  <= /span>media cuadrática

= Consideremos el el problema bajo las siguientes hipótesis F : S × T −→ L2,   <= /span>con S ⊂ L2:

 

1.      =             &nb= sp;          H1: F (X, t) es uniformemente continua sobre ventanas tempor= ales

2.      =             &nb= sp;          H2: F (X, t) satisface la condición de <= span class=3DSpellE>Lipschitz en media cuadrática

                             (2.1)

&nb= sp;

La condición H2 garantiza la continuidad en media cuadrática de F (X, t) con respecto a la primera variable, mientras que H1 garantiza la continuidad de F (X, t) con respecto a la segu= nda variable, de este análisis proviene la desigualdad

 =

 =

Se obtiene la con= tinuidad en media cuadrática de F (X, t) con respecto a las dos variables. Considere= mos el método aleatorio de Euler para el problema de valor inicial

 


 

                                                 =                                                                             (2.2)

 

Donde . Demostraremos que bajo las hipótesis H1 y H2, el mé= todo de Euler (2.2) es convergente en media cuadrática en el sentido de estación fija, i.e. el error de la media cuadrática.

 

 =                                                                   (2.3)

 

= Tiende a cero en L2, cuando .

Bajo las hipótesis H1 y H2, el Teorema 5.1.2 garantiz= a la existencia y unicidad de la solución media cuadrática  y por el Teo= rema 1.0.2, deducimos.

 

                                                =             (2.4)<= /p>

 <= /p>

SX es un conjunto acotado en L2 definido por la soluc= ión teórica exacta del problema de valor inicial,   =                                                                           (2.5)

=  

= Luego, por la hipótesis H1 y la Definición 1.0.1, obtenemos

                                                                     =      (2.6)

=  

y por lo tant= o, toma la forma

                                             (2.7)

=  

= Obtenemos:

y como , la anterior inecuación se escribe como

                                                =                       (2.8)

 

De (2.8), deducimos que {en} es convergente a cer= o en media cuadrática.

Teorema 2.1.1. Bajo las hipótesis H1 y H2, el método aleatorio de Euler (2.2) es convergente en media cuadrática y el error de discretización en, definido por (2.3), satisface la desigualdad (2.8) para

1.4&= nbsp;  Estudio del esquema de Runge-Kutta para ecuaciones diferenc= iales aleatorias

Los métodos numéricos son metodologías que utilizan técnicas meramente algebraicas y aritméticas para resolver de forma aproxim= ada ecuaciones o sistemas de ecuaciones complejos, que analíticamente resultan = muy difíciles e incluso imposible de resolver. Los métodos Runge-Kutta, extienden esta idea geométrica utilizando vari= as derivadas o tangentes intermedias en lugar de solo una, para aproximar la función desconocida. En concreto, un método Runge-Kutt= a explícito de s etapas se puede escribir siguiendo la siguiente estructura:<= o:p>

                                                =                                     (3.1)

 

Donde los cálculos se van realizando de forma recursi= va. De

                                                =          (3.2)

 

En particular, el método de Euler dado en (2.2) se puede escribir como un método de Runge- Kutta de 1 etapa y por tanto requerirá en cada paso d= e una evaluación de la función F. En particular, en este capítulo vamos a centrarnos en el método estándard Runge-Kutta de orden 4, que posiblemente se trata del método Runge-Kutta más conocido y utilizado al mantene= r un buen equilibrio entre coste computacional y precisión. Se trata de un método cuyo orden de convergencia global es 4.

Teorema 3.0.1. Consideremos , definida de en, donde S es un conjunto acotado en L2. Si  satisface las siguientes condiciones

 = es aleatoria uniformemente continua y acotada.

 = satisface la condición de Lipschitz en media cuadrática, = es decir,

                                                =                                      (3.3)

Donde

entonces, el esquema aleatorio de Runge-Kutta de cuarto orden (3.2) es convergente en media cuadrática.

 

Aplicando la regla de L’Hôpital<= /span> se obtiene

 

                                     (3.4= )

 

Por otra parte,

                                                =                                                      (3.5)

 

ya que   cuando .

Por tanto, tomando límites cuando ,  con <= !--[if gte msEquation 12]>nh=3DT, se deduce qu= e en  cuando lo que prueba la convergencia del método de Runge-Kutta.

1.5&= nbsp;  Determinación del desplazamiento por un movimiento terrestre

Dentro de la determinación del efecto sobre una estructura terrestre de una perturbación de tipo terremoto. Supongamos que = la estructura está en reposo en , y sea , el desplazamiento (relativo) horizontal del techo c= on respecto al suelo. Basándose en un modelo lineal idealizado, el desplazamie= nto relativo  se rige por = la ecuación diferencial

 


 

                                                 =                                                           (4.1)

&nb= sp;

donde  = es el proceso estocástico de segundo orden dado por (1.16). La solución en media cuadrática del problema (4.1) tiene la forma:

&nb= sp;

                                                                                    =              (4.2)

Con , y dond= e  es la respue= sta impulsiva

                                                =          (4.3)

&nb= sp;

La esperanza y la función de autocorrelación del proc= eso estocástico de segundo orden  = están dadas por

&nb= sp;

                                                =                                                                 (4.4)

 

Y

<= span lang=3DEN-US style=3D'font-size:11.0pt;font-family:"Century",serif;mso-fare= ast-font-family: Century;mso-bidi-font-family:Century;position:relative;top:6.0pt;mso-text-r= aise: -6.0pt;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language: AR-SA'>                     (4.5)

 

Por lo tanto, de las ecuaciones (4.2)-(4.5), se deduce que

 

=

Y                        (4.6)

 

Dejando  y,= la forma de la matriz vectorial de la ecuación (4.1) es

&nb= sp;

,

donde y ,= se han obtenido en (1.15) y  Como =  = la expresión del método aleatorio de Euler en este caso toma la forma<= /p>

&nb= sp;

Donde se puede denotar la matriz de identidad de tama= ño 2. Señalando que las hipótesis H1 y H2 se cumplen, luego la convergencia en media cuadrática del método aleatorio de Euler está garantizada. De hecho, = H1 se cumple con el Ejemplo 1.0.2 y, según el Lema 1.0.1, se obtiene la condición de Lip= schitz.

&nb= sp;

 

Donde                                        <= /span>

 

Teniendo en cuenta que como , obtenemos que  = y , luego la matriz de covarianza del vector aleatorio =  sería=

 

(4.7)=

 

Siendo                                               

&nb= sp;

 

Donde  está definid= o por (4.5). Teniendo en cuenta que si , denota la matriz cuadrada de tamaño 2 dada por (4.7), entonces  es la varian= za aproximada del proceso estocástico de segundo orden (4.1), obtenida del método aleatorio escalar de Euler. La Figura 4.1 muestra

Para  y la varianza teórica  = dado por (4.6), tomando en (4.1) ξ =3D 0,05 y , , = para dado por (1.8). Dicha gráfica ilustra, que los valores numéricos y exactos están más cerca cuando h disminuye. Este comportamiento está de acu= erdo con la propiedad (1.9).

1.6&= nbsp;  Problema de ruido blanco Gaussiano

Consideremos el problema

 


 

 (4,8)

 

Donde  = es un proceso de ruido blanco Gaussiano con media cero y  = es una variable exponencial aleatoria con el parámetro  , independiente de  = para cada . En este ejemplo tenemos .

&nb= sp;

= La covarianza de  = es

                                                                    =                              <= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-ansi-language:ES'>(4.9)

Donde  = es la función delta generalizada. Siempre existe una convolución con la función delta y juega el mismo papel para la convolución que la unidad para la multiplicación, esto es,

 

&= nbsp;

Tomando , donde <= /span> = es una función  y denota la función característica de la función en el intervalo [0, t], de (4.9= ) se tiene que

&nb= sp;

Para calcular la solución exacta del problema, multiplicaremos ambos lados del resultante de la expresión (4= .9) por  y usando  ten= emos

 

y entonces

<= span lang=3DEN-US style=3D'font-size:11.0pt;font-family:"Century",serif;mso-fare= ast-font-family: Century;mso-bidi-font-family:Century;position:relative;top:6.0pt;mso-text-r= aise: -6.0pt;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language: AR-SA'>                                                =                      (4.10)

 

Ahora, calculamos las aproximaciones  usando el método aleatorio de Runge-Kutta de cuarto orden,

                                                =                             (4.11)

 

Donde         ,=

,

,

,

,

 

Tenemos                                           

 

y procediendo de forma recurren= te obtenemos

                                               <= /span>(4.12)

Donde

,       con

 

Cuadro 1<= /p>

Error absoluto de la esperanza = de  con Euler y Runge-Kutta de orden 4 del Problema de ruido blanco Gaussiano

t=

Euler<= /p>

Runge-Kutta de orden 4

h =3D   = 1

20

h =3D    1

50

h =3D   1

20

h =3D   1

50

0,1

8, 3335 × 10−5

2, 1868 × 10−4

2, 1709 × 10−10<= o:p>

2, 2227 × 10−12<= o:p>

0,2

1, 8389 × 10−3

4, 2813 × 10−3

4, 3522 × 10−10<= o:p>

4, 4555 × 10−12<= o:p>

0,3

2, 9353 × 10−3

1, 8305 × 10−2

6, 5759 × 10−10<= o:p>

6, 7342 × 10−12<= o:p>

0,4

5, 9445 × 10−3

4, 9066 × 10−2

8, 9216 × 10−10<= o:p>

9, 1442 × 10−12<= o:p>

0,5

1, 0166 × 10−2

1, 0493 × 10−1

1, 1604 × 10−9

1, 1963 × 10−11<= o:p>

0,6

1, 5858 × 10−2

1, 9749 × 10−1

1, 5255 × 10−9

1, 6213 × 10−11<= o:p>

0,7

2, 3460 × 10−2

3, 4425 × 10−1

2, 1834 × 10−9

2, 5666 × 10−11<= o:p>

0,8

3, 3671 × 10−2

5, 7317 × 10−1

3, 7346 × 10−9

5, 3311 × 10−11<= o:p>

0,9

4, 7596 × 10−2

9, 3076 × 10−1

7, 9493 × 10−9

1, 4031 × 10−10<= o:p>

1,0

6, 6954 × 10−2

1, 4968

1, 9803 × 10−8

4, 0766 × 10−10<= o:p>

Fuente: El Autor

 

Cuadro 2<= /p>

Error absoluto de la varianza d= e  con Euler y Runge-K= utta de orden 4 del Problema de ruido blanco Gaussiano

 

t=

Euler

Runge-Kutta de orden 4

h =3D    1

20

h =3D    1

50

h =3D    1

20

h =3D    1

50

0,1

6, 0089 × 10−2

5, 9641 × 10−2

1, 0005 × 10−1

1, 0005 × 10−1

0,2

1, 7228 × 10−1

1, 6825 × 10−1

2, 0080 × 10&#= 8722;1

2, 0080 × 10&#= 8722;1

0,3

3, 3247 × 10−1

3, 2120 × 10−1

3, 0408 × 10= 722;1

3, 0408 × 10= 722;1

0,4

5, 3849 × 10−1

5, 1617 × 10−1

4, 1304 × 10−1

4, 1304 × 10−1

0,5

7, 9087 × 10−1

7, 5330 × 10−1

5, 3239 × 10−1

5, 3239 × 10−1

0,6

1, 0930

1, 0353

6, 6898 × 10= 722;1

6, 6898 × 10= 722;1

0,7

1, 4516

1, 3676

8, 3267 × 10−1

8, 3267 × 10−1

0,8

1, 8769

1, 7586

1, 0380

1, 0380

0,9

2, 3832

2, 2199

1, 3075

1, 3075

1,0

2, 9899

2, 7663

1, 6764

1, 6764

Fuente: El Autor

 

 

 

 

Figura 3<= /p>

Esperanza  mediante el método Runge-Kutta de orden 4 y   del Problema de ruido blanco Gaussiano

&nb= sp;

&nb= sp;

&n= bsp;

 

 

 

 

 

 

 


Fuente: El Autor

 

Figura 4<= /p>

Varianza  y  mediante el método Runge-Kutta de orden 4 y   del Problema de ruido blanco Gaussiano

&n= bsp;

 

 

 

 

 

 

 

 


Fuente: El Autor

El error absoluto de la esperan= za y la varianza de  con los métodos de Euler y Runge- Kutta = de orden 4, en   y   , se representan en los Cuadros 1 y 2. Se observa que se obtienen mejores aproximaciones con el método de Runge-Kutta. Las Figuras 3 y 4, representan la comparación de la esperanza y la varianza exacta y aproximada con el método de Runge-Kutta de orden 4 en . Se observa que la aproximació= n de la varianza no es buena ya que el método es apropiado para ruidos cuyo comportamiento muestral no es tan irregular como el ruido blanco (no es diferenciable en ningún punto). Para tratar este tipo de incertidumbre se d= eben aplicar métodos numéricos para ecuaciones diferenciales estocásticas, como = el método de Euler-Maruyama

 

1.7 Estudio de un circuito eléctrico con ruido aleatorio

Consideremos el siguiente circu= ito RC con parámetros constantes:

&n= bsp;


 

                                                 =                                                                         (5.1)

 

Donde  es la carga eléctrica en el tiempo  y  es la variable aleatoria exponencial con= el parámetro ,  independiente de  para cada ,= lo que significa la carga inic= ial en el momento ,= y  son las funciones no aleatorias de la variable tiempo= , lo que significa que el voltaje en el tiempo  y  es el proceso unidimensional del ruido blanco,   es el movimiento unidimensional Browniano y αt es una función no aleatoria que modeliza la intensidad del ruido en el tiempo .

Ahora, resolviendo la ecuación diferencial estocástica, tenemos

                                                =       (5.2)

 

Obsérvese que el miembro izquie= rdo puede escribirse en forma diferencial

                                                =                                   (5.3)

 

Por (= 5.2) y (5.3) tenemos

                                                         (5.4)

 

Ahora, calculamos  por el método aleatorio de Runge-Kutta,

                                                =           (5.5)

Donde

 

Tenemos                                         =

&nb= sp;

y resolviendo esta recurrencia<= span style=3D'mso-spacerun:yes'>                          (5.6)

De (5.4) y (5.6), obtenemos las esperanza y las varianzas de =  y .

                                                                                       (5.7)

 

                                                               =                              (5.8)

 

Y                                                         (5.9)

 

Donde 

&nb= sp;

 

El error absoluto de la esperan= za y la varianza de  con ,  ,  y  C=3D2 está representada en el Cuadro 5.1.

El error absoluto de la esperan= za y varianza de  con ,  ,  y  C=3D2 están representadas en la Figura 5.3 y 5.6.

Cuadro 3<= /p>

Error absoluto de la esperanza y la varianza de  <= i>con  <= i>del estudio de un circuito eléctrico con ruido aleatorio 5.

t

Esperanza

Varianza

0.1=

1, 8186 × 10−9

7, 4434 × 10−8

0.2=

3, 6829 × 10−9

1, 1779 × 10−6

0.3=

5, 6077 × 10−9

5, 6969 × 10−6

0.4=

7, 6086 × 10−9

1, 7054 × 10−5

0.5=

9, 7022 × 10−9

3, 9168 × 10−5

0.6=

1, 1906 × 10−8

7, 5901 × 10−5

0.7=

1, 4240 × 10−8

1, 3052 × 10−4

0.8=

1, 6725 × 10−8

2, 0527 × 10−4

0.9=

1, 9383 × 10−8

3, 0096 × 10−4

1.0=

2, 2239 × 10−8

4, 1676 × 10−4

Fuente: El Autor

Figura 5<= /p>

Esperanza  mediante el método Runge-Kutta de orden 4 y  del estudio de un circuito eléctrico con= ruido aleatorio 5.

 

 

 

 

 

 

 

 

 

 

 


= Fuente: El Autor

 

Figura 6

Varianza   y   mediante el método Runge-Kutta de orden  , del estudi= o de un circuito eléctrico con ruido aleatorio 5

 

=  

=  

=  

=  

=  

=  

=  

=  


= Fuente: El Autor

=  

Conclusiones.

·&nb= sp;     Dentro del estudi= o de la aproximación numérica de ecuaciones diferenciales con el proceso de aleatoriedad, el estudio de la convergencia y sus aplicaciones, se ha plant= eado en primer lugar el esquema aleatorio de Euler que comprende el caso escalar= y matricial demostrando el comportamiento de algunas condiciones en las cuale= s la convergencia referente a la media cuadrática se presenta estable y calcula = la esperanza y la varianza exacta aproximada.

·&nb= sp;     El esquema Euler = permite llegar a la conclusión de que la convergencia lenta y el aspecto de restric= ción de su región de estabilidad absoluta nos hace considerar otros métodos dond= e la convergencia es mayor por lo cual se plantea el estudio adicional del esque= ma aleatorio Runge- Kutta, siendo un método superi= or al de Euler por lo cual su orden de convergencia global es cuarto. =

·&nb= sp;     El método de Rung= e Kutta y Euler permite determinar y analizar varios ca= sos donde se aprecian tipos de ruidos con el comportamiento muestral regular. e= n el caso específico de ruidos blanco que son característicos y muy irregulares = al no ser diferenciales en ningún punto conducen a otro tipo de ecuaciones diferenciales con cierto grado de incertidumbre denominadas ecuaciones diferenciales estocásticas cuya aproximación se aborda con técnicas alternativas como el método de Euler- Maruyama que es considerado de orden superior.

·&nb= sp;     Concluyendo que el método de Runge Kutta de cuarto orden represent= a la mejor elección al momento de proporcionar y trabajar con pequeños márgenes = de errores con respecto a la solución real del problema resaltando la ventaja = de que es fácilmente programable en un software para realizar las diferentes interacciones necesarias, obteniendo resultados en cuanto a la esperanza y varianza de tipo senoidal, la cual ha sido producida por la generación de la corriente alterna.

 

Referencias bibliográficas.

 <= /o:p>

Aldana, S., Vereda, F., Hidalgo-Alvarez, R., & de Vicente, J. (2016). Facile synthesis of magnetic microfibers by directed selfassembly. = Polymer, 93, 61-64.

Azor, Ainhoa, Juan Carlos Cort, Dolores Rosell, Ster E. N. Investigaci, and Tica Septiembre. 2020. “Soluc= ión Numérica de Ecuaciones Diferenciales Con Incertidumbre y Aplicaciones.”

Azorín Penalva, A. (2020). Solución numérica de ecuaciones diferenciales con incertidumbre y aplicaciones.

Bossis, G., Marins, J., Kuzhir, P., Volkova, O., & Zubarev, A. (2015). <= span lang=3DEN-US style=3D'font-size:12.0pt;line-height:115%;font-family:"Times = New Roman",serif; mso-ansi-language:EN-US;mso-no-proof:yes'>Functionalized =

Cortés, J., Jódar L., y Villafuerte L., (2007). Nu= merical solution of random differential equations: a mean square approach. Math Com= put. Model. 45, 757-765.

Henrici, P., (1962). Discrete Variable Methods in Ordinary Differenti= al Equations. John Wiley and Sons, New York.

Lecca, E. R., & Puente, M. M. (2006). Aplicaciones computacionales de las ecuaciones diferencia= les estocásticas. Industrial Data, 9(1), 64-75.

Oksendal, B., (1998). Stochastic Differential Equations: An Introduct= ion with Applications. Springer, New York, 5th edn.

Khodabin M y Rostami M., (2015). Mean square numerical solution of stochastic differential equations by fourth order Run= ge-Kutta method and its application in the electric circ= uits with noise. Advances in Difference Equations, 62, 1-19. [2] L. <= /span>

Villafuerte A (2007). Numerical and Analytical Mean Square Solutions= for Random Differential Models. Tesis Doctoral, Universidad Politécnica de Valencia

Seminario, Ricardo. 20= 12. “Metodos Númericos Para Ingenieria.” Libro 69. <= span lang=3DEN-US>

3Deditorial1.pngSoo= ng, T., (1973). Random Diferential Equations in Science and Engeneering. Academic Press, New York.=

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PARA CITAR EL ARTÍCULO INDEXADO.

 

 = ;

 

Penalva Ainhoa, A., & Yaulema Castañeda= , J. L. (2021). Análisis del caso Solución numérica de ecuaciones diferenciales = con incertidumbre y aplicaciones. ConcienciaDigital, 4(3.1), 253-272. https://doi= .org/10.33262/concienciadigital.v4i3.1.1828

 

3Deditorial1.png

 

 

 

El artículo que se publica es= de exclusiva responsabilidad de los autores y no necesariamente reflejan el pe= nsamiento de la Revista Conciencia Digital.<= o:p>

 

El artículo qu= eda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado por el director de la Revista Conciencia Digital.

3D"logo_catalogo3b.jpg"
 


                                                 =                                                                            =                       

 

 


[1] Escuela Superior Politécnica= de Chimborazo, Ingeniería Eléctrica. Valencia, España   aiazpe@epsa.upv.es, https://orcid.org/0000-0001-7265-1265

[2] Escuela Superior Politécnica = de Chimborazo, Facultad de Ciencias. Riobamba, Ecuador jorge.yaulema@espoch.ed= u.ec, https://orcid.org/0000-0002-0646-3984

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                     =                                                                            =                  ISSN: 2600-5859

                                                    =                              Vol. 4, N°3.1, p. 253-272, agosto, 2021

Análisis Informático                                                      =                              =                                             Página 238

 

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