| Neural network-based H∞ filtering for nonlinear systems with time-delays |
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| 作者单位： | Luan Xiaoli（Inst.of Automation,Jiangnan Univ.,Wuxi 214122,P.R.China） ;
Liu Fei（Inst.of Automation,Jiangnan Univ.,Wuxi 214122,P.R.China） ; |
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| 基金项目： | This project was supported by the National Natural Science Foundation of China (60574001),and Program for New Century Excellent Talents in University (NCET-05-0485) and PIRT Jiangnan. |
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| 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.
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| 关 键 词： | H∞滤波 非线性系统 神经网络 线性矩阵不等式 时延 |
| 收稿时间： | 31 December 2006 |
Neural network-based H∞ filtering for nonlinear systems with time-delays |
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| Authors: | Luan Liu |
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| Institution: | aInst. of Automation, Jiangnan Univ., Wuxi 214122, P. R. China |
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| Abstract: | Anovel 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. |
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| Keywords: | H∞ filtering nonlinear system time-delay neural network linear matrix inequality |
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