In this paper, we first examine limitations of fuzzy neural networks. We find the following. (1) If training errors are the main concerns, Spline can perform better than the generalized dynamic fuzzy neural network (GD-FNN). (2) If the model is nonlinear with a disturbance term, the testing error of the GD-FNN is very large. If the model is chaotic with a disturbance term, both the training error and testing error of the GD-FNN are very large. (3) Using a sequential algorithm as in the GD-FNN, we would always be trapped at the local minima rather than the global minimum. In addition, we propose to use the characteristics among moments and fuzzy rules to identify the density function in advance.
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS