Fuzzy Modeling Using LSTM Cells for Nonlinear Systems
Francisco Vega, Wen Yu · 2020
The data driven black-box and gray-box models, like the neural networks and fuzzy systems, have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper to affront these disadvantages, we use the Takagi-Sugeno fuzzy model and LSTM cells to propose a new fuzzy-network model. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the LSTM. We also 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 observe the advantages of the proposal.