Solution of fractional order diffusion equations with clique neural network

In this paper, the clique artificial neural network method is used to solve the fractional diffusion equation, which is a subclass of partial differential equations. The clique neural network architecture is constructed using input, hidden, and output layers. Several degrees of clique polynomials were used as activation functions, and the output layer was obtained by multiplying them with weight coefficients. Subsequently, the optimization equation was derived, and the exact solution, numerical solution, and error function graphs were obtained using a specialized algorithm. Analysis of the results demonstrates that the clique artificial neural network method provides quicker and more accurate results compared to previously studied methods.

Authors

Publication Details

Journal
Mathematical Modelling and Numerical Simulation with Applications
Published
2025-09-30
DOI
https://doi.org/10.53391/2791-8564.1003
Primary Topic
Fractional Differential Equations Solutions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Solution of fractional order diffusion equations with clique neural network

Ercan Çeli̇k, Mesut Karabacak, M. Kaya
Mathematical Modelling and Numerical Simulation with Applications
Fractional Differential Equations Solutions
article

Solution of fractional order diffusion equations with clique neural network

Ercan Çeli̇k, Mesut Karabacak, M. Kaya
article en

Abstract

In this paper, the clique artificial neural network method is used to solve the fractional diffusion equation, which is a subclass of partial differential equations. The clique neural network architecture is constructed using input, hidden, and output layers. Several degrees of clique polynomials were used as activation functions, and the output layer was obtained by multiplying them with weight coefficients. Subsequently, the optimization equation was derived, and the exact solution, numerical solution, and error function graphs were obtained using a specialized algorithm. Analysis of the results demonstrates that the clique artificial neural network method provides quicker and more accurate results compared to previously studied methods.

Mathematical Modelling and Numerical Simulation with ApplicationsVol. 5(3)
Openalex Percentile: Top 67%
Fractional Differential Equations Solutions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.