Fuzzy Differential Equations for Nonlinear System Modeling With Bernstein Neural Networks

With the fuzzy set theory, the uncertainty of nonlinear systems can be modeled using fuzzy differential equations. The solutions of these equations are the model output, but they are very difficult to obtain. In this paper, we first transform fuzzy differential equations into four ordinary differential equations. Then, we construct neural models with the structure of these ordinary differential equations. Theory analysis and simulation results show that these new models are effective for modeling uncertain nonlinear systems.