Adaptive Hybrid Artificial Intelligence for Robust Prediction and Decision-Making in Engineering Systems Under Uncertainty
DOI:
https://doi.org/10.65204/djes.v3i2.849Keywords:
Adaptive Hybrid Artificial Intelligence; Uncertainty Modeling; Robust Prediction; Reinforcement Learning; Fuzzy Logic; Engineering SystemsAbstract
Designing engineering systems operates under uncertainty, which presents considerable challenges to reliable prediction and decision-making, including noise, model mismatch, and changing environments. Current methods, both purely data-driven AI models and traditional control systems, are limited in their robustness, adaptability, and interpretability, as summarized by the research gap analysis presented in Table 1. To address these research limitations, this paper provides an Adaptive Hybrid Artificial Intelligence (AH-AI) framework that combines deep learning for non-linear feature representation, fuzzy logic for uncertainty modeling, and reinforcement learning for optimal decision-making in one adaptive, integrated structure. The proposed methodology contains an online adaptive update mechanism and an integrated hybrid representation of uncertainty to increase robustness to stochastic disturbance. The AH-AI framework has been validated through a series of multi-scenario simulations and comprehensive Monte Carlo analyses, including evaluation of performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), robustness index, and decision quality. Results indicate that the new technique has considerable advantages compared to conventional methods, including a 30%–45% reduction of prediction errors, as well as greater stability or robustness in an uncertain environment. In addition, statistical evaluation indicates that the new framework consistently produces the same results under many different conditions. These results confirm that our new approach facilitates robust, adaptable, and interpretable decision-making for complex engineering systems despite uncertainty.
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