2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 2 , PP: 23-37, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Doug Young Song 1 *
Doi: https://doi.org/10.54216/AJBOR.110202
As part of the scope of the Artificial Neural Network – Particle Swarm Optimization (ANN-PSO) notion, the computational capability of ANNs is integrated with the optimization potential of PSO. This method proves to be very effective in solving complex non-linear forecasting problems where traditional approaches would not be effective. The data interactions that exist are the ones that are modelled and captured by the ANN component. However, the PSO method is charged with the duty of minimizing the biases and weights used in the ANN to ensure that the model attains the global minimum without being trapped in tiny local minimum. The application of this framework can be extended to cash forecast used in business like the one above in which a days of cash requirement forecast is created based on experience and factors like holidays, pay check effects and working days. However, the given contribution of the PSO element in learning process is linked with continuous transformation of variables under the basic guidelines of swarming intelligence, it makes the learning session of ANN more efficient. Therefore, the degree of accuracy of forecasts that are given by such configurations is improved, especially in the conditions that are in a state of steady evolution. The ANN-PSO model mirrors similar attributes, including its ability to process data in parallel and furthermore, its high compatibility with large-scale data as well as it robustness when working with both non-linear and linear data set. Incorporating the PSO into a model enhances the range of possible solutions and given the peculiarity of the gradient-based approach, it reduces mistakes more effectively than the conventional techniques. They suggested that by applying ANN with PSO the framework act as an efficient tool for prediction and for solving various issues in several fields. In this case, the ANN-PSO strategy suggested here works out to an impressive overall accuracy of over 98% compared to the previous systems.
ANN-PSO , PSO , RMSE , MSE , MAE , MAPE
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