Robust adaptive fault-tolerant learning control for human height-weight prediction based on DNN

In this paper, a distributed robust adaptive confined fault-tolerant optimal control method based on deep neural networks is proposed, aiming to solve the complexity and uncertainty problems in human height and weight prediction. Note that the term 'control' in this work refers to feedback regulation of the iterative learning/optimization dynamics of the predictor (iteration domain), rather than controlling the physical time evolution of human height/weight. In the field of public security technology, accurate prediction of individual physiological characteristics has important application value, especially in crime prevention, individual identification, and behavior analysis. Traditional prediction methods often perform erratically in the face of data noise, environmental changes, and outliers. To this end, this paper combines deep learning and fault-tolerant control theory to propose an efficient and reliable prediction framework by optimizing the robustness and adaptive ability of the predictor. By introducing a limited fault-tolerant mechanism, it can maintain high prediction accuracy and stability under various perturbations and incomplete data conditions. Moreover, we evaluate the proposed framework from three complementary dimensions-statistical similarity, overall predictive performance, and minority-class detection ability-and explicitly acknowledge that these criteria may exhibit trade-offs: improved distributional similarity does not necessarily translate into better decision boundaries, and optimizing overall performance can conflict with minority detection (e.g., recall/F1). Simulation and experimental results show that after 2000 rounds of iterative optimization, the normal and fault-tolerant prediction accuracies of human height for finger length of left and right hands are 98.4% and 97.7%, respectively, and the normal and fault-tolerant prediction accuracies of human body weight are 98.2% and 97.5%, respectively, by combining the 372 sets of data with 30% of data loss caused by human. The accuracy of normal and fault-tolerant prediction of human height was 90.8% and 89.2% for the finger length of the left hand, and the accuracy of normal and fault-tolerant prediction of human weight was 85.6% and 83.3%, respectively. The normal and fault-tolerant prediction accuracies of human height for the finger length of the right hand were 96% and 95.3%, and the normal and fault-tolerant prediction accuracies of human weight were 94.4% and 93.5%, respectively. These findings are most directly applicable to small-to-medium tabular datasets with moderate class imbalance and limited minority samples, which matches the regimes evaluated in this study. This study provides a new idea and technical path for biometric prediction and analysis in the field of public security technology, which has important theoretical significance and practical value.