Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial
  Neural Network
Sales Aribe Jr
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 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 RDV IW technique
greatly improved the new HIV/AIDS forecasting model compared with the two
models.