Abstract
This paper is concerned with studying the robust H ∞ filter design problem for a class of recurrent neural networks with time-varying delay. A delay-dependent criterion involving a scaling parameter is established under which the resulting filtering error system is globally asymptotically stable with a guaranteed performance in the H ∞ sense. The purpose of the introduction of the scaling parameter lies in that the developed result can be efficiently applied to the neural networks with complex dynamic behaviors, which is illustrated by an example.
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Huang, H., Chen, X. (2011). Robust H ∞ Filter Design of Delayed Neural Networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_35
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DOI: https://doi.org/10.1007/978-3-642-21105-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21104-1
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