Abstract
This paper addresses the problem of designing a Markovian H ∞ filter for a class of nonlinear stochastic Markovian jump systems. Firstly, neural networks are employed to approximate the nonlinearities in the different jump modes. Secondly, the overall system is represented by the mode-dependent linear difference inclusion (LDI). Then, a neural network-based Markovian H ∞ filter is developed using the stochastic Lyapunov-Krasovskii stability theory under some linear matrix inequality (LMI) constraints. Finally, a numerical example is worked out to show the usefulness of the theoretical results.
This work is supported by The National Natural Science Foundation of China (NSFC: 60574001)and by Program for New Century Excellent Talents in University (NCET-05-0485).
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Luan, X., Liu, F. (2007). Neural Network-Based H ∞ Filtering for Nonlinear Jump Systems. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_130
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DOI: https://doi.org/10.1007/978-3-540-72395-0_130
Publisher Name: Springer, Berlin, Heidelberg
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