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A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

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Abstract

This paper presents a recurrent fuzzy-neural filter for adaptive noise cancelation. The cancelation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network (DN-FNN). The fuzzy model is based on Takagi–Sugeno–Kang fuzzy rules, whose consequent parts consist of linear combinations of dynamic neurons. The orthogonal least squares method is employed to select the number of rules, along with the number and kind of dynamic neurons that participate in each rule. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.

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Acknowledgments

The Project is co-funded by the European Social Fund and National Resources—(EPEAEK–II) ARXIMHDHS.

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Authors and Affiliations

  1. Department of Informatics and Communications, Technological Educational Institute of Serres, 62124, Serres, Greece

    Paris Mastorocostas, Dimitris Varsamis, Constantinos Hilas & Constantinos Mastorocostas

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  1. Paris Mastorocostas
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  2. Dimitris Varsamis
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  3. Constantinos Hilas
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  4. Constantinos Mastorocostas
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Correspondence to Paris Mastorocostas.

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Mastorocostas, P., Varsamis, D., Hilas, C. et al. A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation. Neural Comput & Applic 17, 521–529 (2008). https://doi.org/10.1007/s00521-007-0129-3

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  • DOI: https://doi.org/10.1007/s00521-007-0129-3

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