Real-time system task flow prediction method based on improved wavelet neural network
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.
wavelet neural networkartificial fish swarm algorithmreal-time systemtraffic prediction
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