Neural network-based H∞ filtering for nonlinear systems with time-delays  

作　　者：Luan Xiaoli Liu Fei 

机构地区：[1]Inst. of Automation, Jiangnan Univ., Wuxi 214122, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2008年第1期141-147,共7页系统工程与电子技术（英文版）

基　　金：the National Natural Science Foundation of China (60574001);Program for New CenturyExcellent Talents in University (NCET-05-0485) and PIRTJiangnan

摘　　要：A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion （LDI）. Finally, based on the LDI model, a neural network-based nonlinear filter （NNBNF） is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality （LMI） constraints. Compared with the existing nonlinear filters, NNBNF is time-invariant and numerically tractable. The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion （LDI）. Finally, based on the LDI model, a neural network-based nonlinear filter （NNBNF） is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality （LMI） constraints. Compared with the existing nonlinear filters, NNBNF is time-invariant and numerically tractable. The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.

关 键 词：H∞ filtering nonlinear system TIME-DELAY neural network linear matrix inequality 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]