Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM

Usama, Muhammad; Lee, In-Young

doi:10.3390/s22218240

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…While some of the aforementioned algorithms excel at generating very accurate symbolic approximations, the reinforcement learning-based deep SR framework proposed in Petersen et al (2021a) is the new standard for exact symbolic function recovery, particularly in the presence of noise (La Cava et al 2021;Matsubara et al 2022). This has resulted in a number of studies in the literature built on this framework (Landajuela et al 2021a(Landajuela et al , 2021aLandajuela et al 2021b;Petersen et al 2021b;Kim et al 2021;DiPietro & Zhu 2022;Du et al (2022;Landajuela et al 2022;Usama & Lee 2022;Zheng et al 2022).…”

Section: Related Work-a Brief Survey Of Modern Srmentioning

confidence: 99%

Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws

Tenachi,

Ibata,

Diakogiannis

2023

ApJ

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Symbolic regression (SR) is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed interest in such approaches, the development of SR methods has not been focused on physics, where we have important additional constraints due to the units associated with our data. Here we present Φ-SO, a physical symbolic optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints. Our system is built, from the ground up, to propose solutions where the physical units are consistent by construction. This is useful not only in eliminating physically impossible solutions but also because the grammatical rules of dimensional analysis enormously restrict the freedom of the equation generator, thus vastly improving performance. The algorithm can be used to fit noiseless data, which can be useful, for instance, when attempting to derive an analytical property of a physical model, and it can also be used to obtain analytical approximations of noisy data. We test our machinery on a standard benchmark of equations from the Feynman Lectures on Physics and other physics textbooks, achieving state-of-the-art performance in the presence of noise (exceeding 0.1%) and show that it is robust even in the presence of substantial (10%) noise. We showcase its abilities on a panel of examples from astrophysics.

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Section: Related Work-a Brief Survey Of Modern Srmentioning

confidence: 99%

Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws

Tenachi,

Ibata,

Diakogiannis

2023

ApJ

View full text Add to dashboard Cite

show abstract

“…While some of the aforementioned algorithms excel at generating very accurate symbolic approximations, the reinforcement learning based deep symbolic regression framework proposed in Petersen et al (2019) is the new standard for exact symbolic function recovery, particularly in the presence of noise (La Cava et al 2021;Matsubara et al 2022). This has resulted in a number of studies in the literature built on this framework (e.g., Du et al 2022;DiPietro & Zhu 2022;Zheng et al 2022;Landajuela et al 2021b;Usama & Lee 2022).…”

Section: Related Work -A Brief Survey Of Modern Symbolic Regressionmentioning

confidence: 99%

Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws

Tenachi¹,

Ibata²,

Diakogiannis³

2023

Preprint

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Symbolic Regression is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed interest in such approaches, efforts have not been focused on physics, where we have important additional constraints due to the units associated with our data. Here we present Φ-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints. Our system is built, from the ground up, to propose solutions where the physical units are consistent by construction. This is useful not only in eliminating physically impossible solutions, but because it restricts enormously the freedom of the equation generator, thus vastly improving performance. The algorithm can be used to fit noiseless data, which can be useful for instance when attempting to derive an analytical property of a physical model, and it can also be used to obtain analytical approximations to noisy data. We showcase our machinery on a panel of examples from astrophysics.

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A Lightweight Deep Learning Model for Automatic Modulation Classification Using Residual Learning and Squeeze–Excitation Blocks

et al. 2023

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Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise. However, their efficacy depends on the complexity of the algorithm, which can be characterized by the number of parameters. In this research, we presented a deep learning algorithm for AMC, inspired by residual learning, which has remarkable accuracy and great representational ability. We also employed a squeeze-and-excitation network that is capable of exploiting modeling interconnections between channels and adaptively re-calibrates the channel-wise feature response to improve performance. The proposed network was designed to meet the accuracy requirements with a reduced number of parameters for efficiency. The proposed model was evaluated on two benchmark datasets and compared with existing methods. The results show that the proposed model outperforms existing methods in terms of accuracy and has up to 72.5% fewer parameters than convolutional neural network designs.

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Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM

Cited by 3 publications

References 25 publications

Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws

Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws

Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws

A Lightweight Deep Learning Model for Automatic Modulation Classification Using Residual Learning and Squeeze–Excitation Blocks

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