Cascade-forward neural network for volterra integral equation solution
Author
al-Rabii, Shayma Akram Hantush
Source
Ibn al-Haitham Journal for Pure and Applied Science
Issue
Vol. 34, Issue 3 (30 Sep. 2021), pp.104-115, 12 p.
Publisher
University of Baghdad College of Education for Pure Science / Ibn al-Haitham
Publication Date
2021-09-30
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Topics
Abstract EN
The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation.
The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles.
One of these methods employ neural network for obtaining the solution.
This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions.
This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network.
Cascade-forward neural network is trained multi times to obtain the desired output, the training of cascade-forward neural network model terminal when there is no enhancement in result.
The model combines all training cascade-forward neural network to obtain the best result.
This method proved its successful in training and testing cascade-forward neural network for obtaining the desired output of numerical solution of volterra integral equation for multi intervals.
Cascade-forward neural network model measured by calculating MSE to compute the degree of error at each training The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation.
The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles.
One of these methods employ neural network for obtaining the solution.
This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions.
This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network.
Cascade-forward neural network is trained multi times to obtain the desired output, the training of cascade-forward neural network model terminal when there is no enhancement in result.
The model combines all training cascade-forward neural network to obtain the best result.
This method proved its successful in training and testing cascade-forward neural network for obtaining the desired output of numerical solution of volterra integral equation for multi intervals.
Cascade-forward neural network model measured by calculating MSE to compute the degree of error at each training time.
American Psychological Association (APA)
al-Rabii, Shayma Akram Hantush. 2021. Cascade-forward neural network for volterra integral equation solution. Ibn al-Haitham Journal for Pure and Applied Science،Vol. 34, no. 3, pp.104-115.
https://search.emarefa.net/detail/BIM-1255660
Modern Language Association (MLA)
al-Rabii, Shayma Akram Hantush. Cascade-forward neural network for volterra integral equation solution. Ibn al-Haitham Journal for Pure and Applied Science Vol. 34, no. 3 (2021), pp.104-115.
https://search.emarefa.net/detail/BIM-1255660
American Medical Association (AMA)
al-Rabii, Shayma Akram Hantush. Cascade-forward neural network for volterra integral equation solution. Ibn al-Haitham Journal for Pure and Applied Science. 2021. Vol. 34, no. 3, pp.104-115.
https://search.emarefa.net/detail/BIM-1255660
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 115
Record ID
BIM-1255660