Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network
Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-1 |
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| Year of Publication : 2024 | ||
| Author : Sales G. Aribe Jr |
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| DOI : 10.14445/22315381/IJETT-V72I1P102 | ||
How to Cite?
Sales G. Aribe Jr, "Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 11-19, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P102
Abstract
Decision-making and planning have long relied heavily on AI-driven forecasts. The government and the general public are working to minimize the risks while maximizing benefits in the face of potential future public health uncertainties. This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique to improve the convergence of Particle Swarm Optimization (PSO) and the accuracy of Artificial Neural Network (ANN). The IW technique, inspired by the motions of a golf ball, modified the particles’ velocities as they approached the solution point to a parabolically descending structure. Simulation results revealed that the proposed forecasting model with [0.4, 0.9] combination of alpha and alpha_dump exhibits a 6.36% improvement in position error and 11.75% improvement in computational time compared to the old model, thus improving its convergence. It reached the optimum level at minimal steps with a 12.50% improvement as against the old model since it provides better velocity averages when speed stabilization occurs at the 24th iteration. Meanwhile, the computed p-values for NRMSE (0.04889174), MAE (0.02829063), MAPE (0.02226053), WAPE (0.01701545), and R2 (0.00000021) of the proposed algorithm are less than the set 0.05 level of significance; thus the values indicated a significant result in terms of accuracy performance. Applying the modified ANN-PSO using the RDV IW technique greatly improved the new HIV/AIDS forecasting model compared with the two models.
Keywords
Artificial Neural Network, Particle Swarm Optimization, Inertia Weight, forecasting, HIV.
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