An Interpretation of Long Short-Term Memory Recurrent Neural Network for Approximating Roots of Polynomials

An Interpretation of Long Short-Term Memory Recurrent Neural Network for Approximating Roots of Polynomials

This paper aims to present a flexible method for interpreting the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) for the relational structure between the roots and the coefficients of a polynomial. A database is first developed for randomly selected inputs based on the degrees of the uni...

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Bibliographic Details
Main Authors: Madiha Bukhsh, Muhammad Saqib Ali, Muhammad Usman Ashraf, Khalid Alsubhi, Weiqiu Chen
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Long short-term memory
recurrent neural network
deep neural network
adaptive moment estimation algorithm
error cost function
Online Access:https://ieeexplore.ieee.org/document/9729726/

Internet

https://ieeexplore.ieee.org/document/9729726/

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