DOAJ Open Access 2023

Observer-Based State Estimation for Recurrent Neural Networks: An Output-Predicting and LPV-Based Approach

Wanlin Wang Jinxiong Chen Zhenkun Huang

Abstrak

An innovative cascade predictor is presented in this study to forecast the state of recurrent neural networks (RNNs) with delayed output. This cascade predictor is a chain-structured observer, as opposed to the conventional single observer, and is made up of several sub-observers that individually estimate the state of the neurons at various periods. This new cascade predictor is more useful than the conventional single observer in predicting neural network states when the output delay is arbitrarily large but known. In contrast to examining the stability of error systems solely employing the Lyapunov–Krasovskii functional (LKF), several new global asymptotic stability standards are obtained by combining the application of the Linear Parameter Varying (LPV) approach, LKF and convex principle. Finally, a series of numerical simulations verify the efficacy of the obtained results.

Topik & Kata Kunci

Applied mathematics. Quantitative methods Mathematics Electronic computers. Computer science

Penulis (3)

Wanlin Wang

Jinxiong Chen

Zhenkun Huang

Format Sitasi

APA MLA BibTeX

Wang, W., Chen, J., Huang, Z. (2023). Observer-Based State Estimation for Recurrent Neural Networks: An Output-Predicting and LPV-Based Approach. https://doi.org/10.3390/mca28060104

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/mca28060104
Akses: Open Access ✓