H? filtering for nonlinear systems via neural networks ?
A novel H? filter design methodology has been presented for a general class of nonlinear systems. Different from existing nonlinear filtering design, the nonlinearities are approximated using neural networks, and then are modeled based on linear difference inclusions, which makes the structure of the desired filter simpler and parameter turning easier and has the advantages of guaranteed stability, numeral robustness, bounded estimation accuracy. A unified framework is established to solve the addressed H? filtering problem by exploiting linear matrix inequality (LMI) approach. A numerical example shows that the filtering error systems will work well against bounded error between a nonlinear dynamical system and a multilayer neural network.
h? filtering nonlinear systems neural networks linear matrix inequalities (lmis)