A Type-2 Fuzzy State Observer Model for Non-Stationary Dynamic System Identification: An Incremental Learning Method with Noise Handling
Anderson Pablo Freitas Evangelista, Ginalber Luiz de Oliveira Serra · IntechOpen eBooks · 2024
Real-world identification involves dealing with challenges such as system complexity, noise, and uncertainties. In this context, a method for incremental learning is suggested, utilizing an evolving type-2 state observer fuzzy model. The process involves structure learning through an evolving type-2 multiscaling clustering approach, eliminating the need for data normalization. The estimation of linear state observer models for each rule is achieved using observer Markov parameters computed via a Type-2 Instrumental Variable (T2-IV) algorithm. For obtaining the instruments for the T2-IV algorithm, a recursive moving-average filter is used. Benchmark and online identification tasks are conducted to demonstrate the practicality and robustness of the proposed methodology, with performance comparisons against existing methodologies.