1973
DOI: 10.1016/0041-5553(73)90097-9
|View full text |Cite
|
Sign up to set email alerts
|

Solution of convolution-type integral equations of the first kind in the multidimensional case

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

1986
1986
1986
1986

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 2 publications
0
1
0
Order By: Relevance
“…At the solution of integral equations of the first kind of convolution type with kernels of the most general types, with the aid of summation Fourier integrals by factors with parameter a, under certain properties of the noise (error) in the right-hand side of the equation, one has obtained the systematic (depending on ~) and the random (depending on the noise) error components of the regularized solution, and, by setting them equal to each other, one selects an "almost optimal" value of a [30,34,307,349]; at the absence of information on the magni-tude of the maximum of the derivative of the exact solution the method can be improved [40] on the basis of the minimization of the sensitivity function M(llSxJSln all); here and in the sequel, M is the mathematical expectation symbol.…”
Section: Methodsmentioning
confidence: 99%
“…At the solution of integral equations of the first kind of convolution type with kernels of the most general types, with the aid of summation Fourier integrals by factors with parameter a, under certain properties of the noise (error) in the right-hand side of the equation, one has obtained the systematic (depending on ~) and the random (depending on the noise) error components of the regularized solution, and, by setting them equal to each other, one selects an "almost optimal" value of a [30,34,307,349]; at the absence of information on the magni-tude of the maximum of the derivative of the exact solution the method can be improved [40] on the basis of the minimization of the sensitivity function M(llSxJSln all); here and in the sequel, M is the mathematical expectation symbol.…”
Section: Methodsmentioning
confidence: 99%