A data-driven TSK fuzzy system with adaptive rule construction for high-dimensional regression problems

High-dimensional functional regression problems are important topics in the field of complex system modeling and prediction. However, traditional machine learning algorithms often struggle to effectively handle high-dimensional input spaces and nonlinear mapping relationships, resulting in poor model performance and computational efficiency. To address this challenge, this paper proposes an innovative regression modeling approach based on fuzzy logic. This method cleverly utilizes the advantages of fuzzy logic to achieve nonlinear mapping from high-dimensional inputs to outputs through data-driven fuzzy rule base construction and efficient fuzzy inference mechanisms. In the rule base construction phase, we use fuzzy partitioning and clustering algorithms to automatically extract a compact set of rules. In the inference phase, we employ a computationally efficient TSK (Takagi-Sugeno-Kang) inference mechanism and parameter learning algorithm to achieve adaptive optimization of model performance. Extensive experiments demonstrate that the proposed fuzzy logic regression model significantly outperforms traditional machine learning algorithms in terms of regression performance and computational efficiency, exhibiting excellent generalization capability and robustness. Furthermore, through parameter sensitivity analysis, we reveal the inherent connections between model performance and key factors, providing important insights for practical applications. The research findings of this paper provide a novel and effective solution for high-dimensional functional regression problems, which is expected to advance the development of complex system modeling and prediction technologies.