Using Feed Forward Neural Network to Solve Eigenvalue Problems | Zendy

‎L‎. ‎N‎. ‎M‎. Tawfiq | Zendy; Othman Mahdi Salih | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Using Feed Forward Neural Network to Solve Eigenvalue Problems

Author(s) -

‎L‎. ‎N‎. ‎M‎. Tawfiq,

Othman Mahdi Salih

Publication year - 2014

Publication title -

conference papers in science

Language(s) - English

Resource type - Journals

eISSN - 2356-6108

pISSN - 2356-6094

DOI - 10.1155/2014/906376

Subject(s) - artificial neural network , eigenvalues and eigenvectors , computer science , ordinary differential equation , backpropagation , bayesian probability , bayesian network , artificial intelligence , mathematical optimization , machine learning , algorithm , differential equation , mathematics , mathematical analysis , physics , quantum mechanics

The aim of this paper is to presents a parallel processor technique for solving eigenvalue problem for ordinary differential equations using artificial neural networks. The proposed network is trained by back propagation with different training algorithms quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. The next objective of this paper was to compare the performance of aforementioned algorithms with regard to predicting ability.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research