In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller.
The yolo series is the prevalent algorithm for target identification at now. Nevertheless, due to the high real-time, mixed target parity, and obscured target features of vehicle target recognition, missed detection and incorrect detection are common. It enhances the yolo algorithm in order to enhance the network performance of this method while identifying vehicle targets. To properly portray the improvement impact, the yolov4 method is used as the improvement baseline. First, the structure of the DarkNet backbone network is modified, and a more efficient backbone network, FBR-DarkNet, is presented to enhance the effect of feature extraction. In order to better detect obstructed cars, a thin feature layer for focused detection of tiny objects is added to the Neck module to increase the recognition impact. The attention mechanism module CBAM is included to increase the model’s precision and speed of convergence. The lightweight network replaces the MISH function with the H-SWISH function, and the improved algorithm improves by 4.76 percentage points over the original network on the BDD100K data set, with the mAP metrics improving by 8 points, 8 points, and 7 points, respectively, for the car, truck, and bus categories. Compared to other newer and better algorithms, it nevertheless maintains a pretty decent performance. It satisfies the criteria for real-time detection and significantly improves the detection accuracy.
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