提出平滑自学习方法，让动态系统模型随变化连续调整。
Fuzzy Model Identification and Self Learning with Smooth Compositions
- 基于平滑组合构建可自适应调整的模糊模型
- 在模型精度与计算开销间取得良好平衡
- 适合需持续优化的化工等非线性系统建模
本文针对动态系统中参数变化与不确定性问题，提出一种平滑模型辨识与自学习策略。通过在连续平滑曲面上追踪系统变化，使模型能够动态适应参数波动。该方法支持参数在平滑表面上自适应优化，为后续基于梯度的控制算法（如MPC或鲁棒控制）提供联合建模-控制框架。相比早期平滑模糊建模结构，本方法在模型最优性与计算负荷之间实现了更优权衡。实验验证了其在测试问题及化工非线性动态过程中的有效性。
原文摘要 · Abstract (English)
This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.
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