Computer network fault diagnosis based on improved TLBO and MBGD optimization
DOI:
https://doi.org/10.15837/ijccc.2026.2.7068Keywords:
mini batch gradient descent algorithm, network malfunction, diagnosis, convolutional neural network, hybrid teaching and learning optimization algorithmAbstract
With the increasing complexity and frequency of computer network faults, efficient fault diagnosis is crucial to the reliability and security of the network. To this end, this paper proposes an advanced fault diagnosis method based on the improved Teaching Learning-based Optimization (TLBO) algorithm and the Mini Batch Gradient Descent (MBGD) algorithm under the framework of Convolutional Neural Network (CNN). Different from the traditional CNN-based methods, this method innovatively integrates the TLBO algorithm with the Differential Evolution (DE) strategy, optimizes hyper-parameters and training convergence, and significantly improves the detection accuracy and speed. Meanwhile, MBGD can effectively refine the model parameters and prevent convergence to local minima. The experimental results using the public dataset prove the effectiveness of this method, achieving a high classification accuracy rate of up to 88.3% and significantly reducing the false detection rate to below 0.20%. Compared with the traditional CNN model and the latest methods, this method has a faster convergence speed, model stability and fault diagnosis performance. This research provides a robust solution for real-time fault detection and diagnosis, significantly enhancing the reliability and security of complex network systems.
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