In this paper, an improved gray wolf optimization linear active disturbance rejection control (IGWO-LADRC) had been proposed by improving the grey wolf population initialization, convergence factor, and grey wolf position updating strategy of the traditional grey wolf optimization algorithm. The problems of standard gray wolf optimization algorithm in linear active disturbance rejection control parameters tuning, such as low efficiency of optimization search and easy fall into local optimal solution, had been solved. A brushless DC motor control system based on IGWO-LADRC and a brushless DC motor control system based on gray wolf optimization linear active disturbance rejection control (GWO-LADRC) had been built. The comparison had shown that IGWO-LADRC had a fast optimization-seeking speed, high control accuracy, quick response speed, and strong anti-interference ability.
Emotion recognition from physiological signals is a crucial area in affective computing. However, traditional CNN models face challenges in accuracy and efficiency. This paper proposes a lightweight IGC-CNN model that integrates interleaved group convolutions with the LeNet-5 network. Experimental results using EEG, EMG, and EDA signals collected across happiness, sadness, and fear states show that IGC-CNN achieves an average accuracy of 94.74%, outperforming traditional CNNs by 10.06%. Statistical analysis confirms the significance of this improvement (P < 0.01). Evaluation metrics such as AUC, precision, recall, and F1 score further validate the superior performance of IGC-CNN. This study suggests that IGC-CNN is a promising approach for multimodal physiological signal-based emotion recognition.
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