On the adaptation of recurrent neural networks for system identification

Forgione, Marco; Muni, Aneri; Piga, Dario; Gallieri, Marco

doi:10.1016/j.automatica.2023.111092

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Therefore, another ANN variant, RNN, was proposed to analyze and process the triboelectric signals containing temporal information [ 163 ]. RNN is suitable for describing outputs in continuous states, exhibiting memory capabilities [ 164 ]. In RNN, neurons not only receive information from other neurons but also themselves, forming a network structure with loops.…”

Section: ML For Tengsmentioning

confidence: 99%

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Li,

Wei,

Wang

2024

Nanomaterials

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The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid–solid TENG and liquid–solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

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Section: ML For Tengsmentioning

confidence: 99%

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Li,

Wei,

Wang

2024

Nanomaterials

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“…At present, machine vision methods based on various advanced and accurate recognition schemes such as the recurrent neural network, deep neural network, and CNN can effectively and accurately monitor harsh working environments and are suitable for this study. (11)(12)(13)(14)(15)…”

Section: Introduction 11 Hot Steel-bar Stack Accidentsmentioning

confidence: 99%

A Novel Integral Image Recognition Method and System with Verification Measurement by Sensors for Hot Steel-Bar Stack Accident Detection

Ren,

Wang,

et al. 2024

Sensors and Materials

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“…The adoption of Artificial Neural Networks (ANNs) for system identification has gained prominence due to their adeptness in modeling intricate nonlinear systems. ANNs can approximate any continuous function with remarkable precision, rendering them an ideal instrument for system identification [22][23][24][25]. Nevertheless, the training of ANNs can be computationally demanding and necessitates substantial data volumes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Modeling Incommensurate Fractional Order Systems Using Fractional Neural Networks

Kumar,

Mehta,

Cirrincione

2023

Mathematics

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This research explores the application of the Riemann–Liouville fractional sigmoid, briefly RLFσ, activation function in modeling the chaotic dynamics of Chua’s circuit through Multilayer Perceptron (MLP) architecture. Grounded in the context of chaotic systems, the study aims to address the limitations of conventional activation functions in capturing complex relationships within datasets. Employing a structured approach, the methods involve training MLP models with various activation functions, including RLFσ, sigmoid, swish, and proportional Caputo derivative PCσ, and subjecting them to rigorous comparative analyses. The main findings reveal that the proposed RLFσ consistently outperforms traditional counterparts, exhibiting superior accuracy, reduced Mean Squared Error, and faster convergence. Notably, the study extends its investigation to scenarios with reduced dataset sizes and network parameter reductions, demonstrating the robustness and adaptability of RLFσ. The results, supported by convergence curves and CPU training times, underscore the efficiency and practical applicability of the proposed activation function. This research contributes a new perspective on enhancing neural network architectures for system modeling, showcasing the potential of RLFσ in real-world applications.

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On the adaptation of recurrent neural networks for system identification

Cited by 8 publications

References 7 publications

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

A Novel Integral Image Recognition Method and System with Verification Measurement by Sensors for Hot Steel-Bar Stack Accident Detection

A Novel Approach to Modeling Incommensurate Fractional Order Systems Using Fractional Neural Networks

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