Real time system task flow prediction method based on improved wavelet neural network

DOI:	10.11809/bqzbgcxb2024.06.029
Keywords:	wavelet neural network; artificial fish swarm algorithm; real time system; traffic prediction
Abstract:	This study addresses the challenge of unreliable scheduling for non periodic real time tasks in aerospace equipment real time systems, arising from their unpredictable nature. The primary focus lies in predicting non periodic task traffic within these systems. To achieve this, we establish a task traffic prediction model by leveraging wavelet neural networks and considering the specific characteristics of aerospace equipment real time systems. Furthermore, we propose an optimization approach that employs the artificial fish swarm algorithm to fine tune the parameters of the wavelet prediction model. This optimization strategy aims to circumvent local optima and leads to an enhanced wavelet neural network based task traffic prediction system utilizing the artificial fish swarm algorithm. To validate the effectiveness of the proposed model, we conduct comparative simulation experiments for real time task traffic prediction. The results unequivocally demonstrate that the developed real time system task traffic prediction system, based on the improved wavelet neural network, achieves significantly higher prediction accuracy for non periodic real time tasks, outperforming the original wavelet neural network model and T S fuzzy neural network model.
Issue:	Vol. 45 No. 6 (2024)
Published:	2024-06-28
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