PERFORMANCE EVALUATION OF TRAINING FUNCTIONS IN NEURAL NETWORKS FOR OPTIMAL BIOMASS ENERGY CONTENT PREDICTION

Authors

  • Aisha Saeed Bello
    Department of Electrical and Computer Engineering, Faculty of Engineering, Baze University, Abuja, Nigeria
  • Usman Alhaji Dodo

Keywords:

Artificial neural networks; biomass; higher heating value; transfer functions; ultimate analysis

Abstract

This study compares the performance of ten training functions in artificial neural networks for predicting the higher heating value (HHV) of biomass using ultimate analysis. A 5-10-1 feed-forward back-propagation neural network architecture was implemented, with data normalized and divided into 70% training and 30% testing. Model accuracy was assessed using the coefficient of determination (R²), mean squared error (MSE), and mean absolute error (MAE). Results obtained showed that the Bayesian Regularization algorithm (trainbr, M3) outperformed other models, achieving an R² of 0.8358, MSE of 0.001432, and MAE of 0.000321 in the training phase, and an R² of 0.9451, MSE of 0.003077, and MAE of 0.001225 in the testing phase. The Levenberg‑Marquardt algorithm (trainlm, M1) followed closely, while the Gradient Descent algorithms (traingd and traingdm) gave the weakest results with low or negative R² values. The findings demonstrate that the trainbr function provides superior predictive reliability for biomass HHV.

Dimensions

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Radar Plots Using MSE (a) Training Phase (b) Testing Phase

Published

10-11-2025

How to Cite

Saeed Bello, A., & Alhaji Dodo, U. (2025). PERFORMANCE EVALUATION OF TRAINING FUNCTIONS IN NEURAL NETWORKS FOR OPTIMAL BIOMASS ENERGY CONTENT PREDICTION. FUDMA JOURNAL OF SCIENCES, 9(11), 441 – 448. https://doi.org/10.33003/fjs-2025-0911-4009

Issue

Vol. 9 No. 11 (2025): FUDMA Journal of Sciences - Vol. 9 No. 11

Section

Research Articles
Copyright & Licensing

Copyright (c) 2025 Aisha Saeed Bello, Usman Alhaji Dodo

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Saeed Bello, A., & Alhaji Dodo, U. (2025). PERFORMANCE EVALUATION OF TRAINING FUNCTIONS IN NEURAL NETWORKS FOR OPTIMAL BIOMASS ENERGY CONTENT PREDICTION. FUDMA JOURNAL OF SCIENCES, 9(11), 441 – 448. https://doi.org/10.33003/fjs-2025-0911-4009

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