Offline Identification of T-S Models

For offline identification of T-S models, the procedure requires only available data to directly identify both the center parameters (typically through clustering algorithms like fuzzy c-means) and consequent parameters (usually via linear regression methods). The implementation typically involves data preprocessing, fuzzy partitioning of input space, parameter estimation using least squares optimization, and model validation. This approach is highly practical since data is easily obtainable in real-world applications. Additionally, error curve analysis provides valuable insights for model refinement, allowing for systematic improvement of model accuracy through iteration and parameter adjustment. Consequently, this data-driven T-S model identification method holds significant practical value in engineering applications, with code implementations often featuring modular functions for data loading, parameter initialization, optimization loops, and visualization of error convergence.