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A new approach for \(H_{\infty }\) deconvolution filtering of 2D systems described by the Fornasini–Marchesini and discrete moments

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Abstract

This study proposes a new approach of \(H_{\infty }\) deconvolution filtering of 2D system described by Fornasini–Marchesini model and Tchebichef moments. The challenge of this method is to generate an unknown 2D signal by transmission channel. This canal is mobilized by convolution system and deconvolution filter to rebuild the output signal. To resolve this problem, firstly, we use the Tchebichef moments to extract the feature vectors of a medicinal Cannabis sativa plant for generating the input system with the minimum information. Next, we implement the system with the model of Fornasini–Marchesini for convolution and deconvolution. However, the free matrix variables are used to eliminate coupling between Lyapunov matrix and system matrices to obtain sufficient conditions in linear matrix inequality form to ensure the desired stability and performance of the error systems. Experimental results show that the new approach for \(H_{\infty }\) deconvolution filtering of 2D systems described by the Fornasini–Marchesini model and Tchebichef moments achieves good performance than the recent works.

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Authors and Affiliations

  1. Faculty of Sciences Dhar el Mahraz, Computer Science, Signals, Automation and Cognitivism Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco

    Bensalem BOUKILI, Abderrahim AMRANI, Amal ZOUHRI, Ismail BOUMHIDI & Abdelaziz HMAMED

  2. High Normal School, Department of Mathematics and Computer Sciences, Laboratory of Computer Science and Physics Interdisciplinary, Sidi Mohammed Ben Abdellah University, Fez, Morocco

    Mostafa EL MALLAHI

Authors
  1. Bensalem BOUKILI
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  2. Mostafa EL MALLAHI
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  3. Abderrahim AMRANI
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  4. Amal ZOUHRI
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  5. Ismail BOUMHIDI
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  6. Abdelaziz HMAMED
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Correspondence to Mostafa EL MALLAHI.

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BOUKILI, B., EL MALLAHI, M., AMRANI, A. et al. A new approach for \(H_{\infty }\) deconvolution filtering of 2D systems described by the Fornasini–Marchesini and discrete moments. Pattern Anal Applic 25, 63–76 (2022). https://doi.org/10.1007/s10044-021-01030-7

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