Type-2 Fuzzy LSTM for Nonlinear System Modeling
Francisco Vega, Xiaoou Li, Brisbane Ovilla-Martínez, Wen Yu · 2023
Type-2 fuzzy systems have a great adoption in different branches of engineering, due to the fact that this type of fuzzy systems are very well suited to tasks related to nonlinear systems. Data driven models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we show the advantages of type-2 fuzzy systems over type-1 fuzzy systems in modeling nonlinear systems. We combine Type-2 Takagi-Sugeno fuzzy model with the popular deep learning model, LSTM (long-short term memory), to overcome the disadvantages fuzzy model and neural network model. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to show the advantages of the type-2 fuzzy LSTM neural networks.