Deterministic learning from sampling data

Wu, Weiming; Yuan, Chengzhi

doi:10.1016/j.neucom.2019.05.044

Cited by 36 publications

(14 citation statements)

References 34 publications

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“…In this section, a sampled-data deterministic learning algorithm [38] is firstly presented for modeling of dynamical patterns consisting of sampling sequences. Some important steps about the sampled-data deterministic learning algorithm will be introduced for the completeness of presentation.…”

Section: Modeling and Similarity Of Dynamical Patterns Consisting Of mentioning

confidence: 99%

“…denotes the tracking errors. By using the above identifiers (5) and weight update law (6), it is rigorously proven in [38] that the system dynamics of sampling sequences can be locally-accurately modeled and stored in constant RBFNs. Lemma 1 ( [38]).…”

Section: Modeling and Similarity Of Dynamical Patterns Consisting Of mentioning

confidence: 99%

“…By using the above identifiers (5) and weight update law (6), it is rigorously proven in [38] that the system dynamics of sampling sequences can be locally-accurately modeled and stored in constant RBFNs. Lemma 1 ( [38]). Consider the sampling sequence φ s sampled from the dynamical system (1) with constant sampling period T , dynamical RBFN identifiers (5), and NN weights update laws (6), wherê W s i (0) = 0, ∀i ∈ {1, .…”

Section: Modeling and Similarity Of Dynamical Patterns Consisting Of mentioning

confidence: 99%

“…In this paper, based on deterministic learning, we further propose a method for rapid recognition of dynamical patterns consisting of sampling sequences. First, for the sampling sequences yielded by sampling a periodic or recurrent trajectory (a dynamical pattern) generated from a nonlinear dynamical system, a sampled-data deterministic learning algorithm [37,38] can be employed to perform accurate modeling/identification of the inherent system dynamics. Second, a definition to characterize similarities between sampling sequences (dynamical patterns) is formulated based on differences in system dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Rapid dynamical pattern recognition for sampling sequences

Wang

Yuan

2021

Sci. China Inf. Sci.

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Section: Modeling and Similarity Of Dynamical Patterns Consisting Of mentioning

confidence: 99%

Section: Modeling and Similarity Of Dynamical Patterns Consisting Of mentioning

confidence: 99%

Section: Modeling and Similarity Of Dynamical Patterns Consisting Of mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid dynamical pattern recognition for sampling sequences

Wang

Yuan

2021

Sci. China Inf. Sci.

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“…Mainly, neural networks and fuzzy logic systems have made significant progress in designing solutions for complex control systems. In [14], deterministic learning is presented to identify nonlinear dynamic systems based on sampled data sequences with simulation results applied to chaotic systems using radial basis neural networks. In [15], a backstepping controller based on a fuzzy observer for the control of a nonlinear system with strict second-order feedback is presented.…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

et al. 2020

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This work introduces a neural-feedback control scheme for discrete-time quantized nonlinear systems with time delay. Traditionally, a feedback controller is designed under ideal assumptions that are unrealistic for real-work problems. Among these assumptions, they consider a perfect communication channel for controller inputs and outputs; such a perfect channel does not consider delays, or noise introduced by the sensors and actuators even if such undesired phenomena are well-known sources of bad performance in the systems. Moreover, traditional controllers are also designed based on an ideal plant model without considering uncertainties, disturbances, sensors, actuators, and other unmodeled dynamics, which for real-life applications are effects that are constantly present and should be considered. Furthermore, control system design implemented with digital processors implies sampling and holding processes that can affect the performance; considering and compensating quantization effects of measured signals is a problem that has attracted the attention of control system researchers. In this paper, a neural controller is proposed to overcome the problems mentioned above. This controller is designed based on a neural model using an inverse optimal approach. The neural model is obtained from available measurements of the state variables and system outputs; therefore, uncertainties, disturbances, and unmodeled dynamics can be implicitly considered from the available measurements. This paper shows the performance and effectiveness of the proposed controller presenting real-time results obtained on a linear induction motor prototype. Also, this work includes stability proof for the whole scheme using the Lyapunov approach.

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ECG‐based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease

Wang

Sun

Kim

et al. 2023

Clinical Cardiology

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Background: The cardiodynamicsgram (CDG), a novel noninvasive method, extracts dynamic ST-T segment information from an electrocardiogram (ECG) through deterministic learning. Hypothesis:The CDG can reflect anomalous functional information in coronary artery disease (CAD).Methods: We retrospectively enrolled 456 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) from January 2020 to 2022, followed immediately by standard 12-lead ECG acquisition. Positivity for CAD were defined as CCTA ≥ 50% or CT-derived fractional flow reserve (CT-FFR) ≤ 0.8. A CDG value <0 was considered negative; otherwise, it was considered positive. We also evaluated the diagnostic performance of the CDG in the ECGdiagnosis-negative subgroup and in patients who had undergone invasive coronary angiography (ICA) after CCTA.Results: Of 362 patients, 168 (46.41%) were positive for CAD, and 178 (49.17%) were men. The median age was 59 (52−66) years. The accuracy of the CDG in the diagnosis of CAD was 79.56%, with a sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of 75.60%, 82.99%, and 0.836 (95% CI: 0.794−0.878), respectively. Similarly, in the ECG-diagnosis-negative subgroup (n = 223), the accuracy of the CDG was 80.27%, with an AUC of 0.842 (95% CI: 0.790−0.895). Among the 11 patients with CAD confirmed by ICA, 10 were diagnosed positive by the CDG. Furthermore, the CDG values and CT-FFR were correlated (r = −.395; p < .001). Conclusions:The ECG-based CDG has relatively high specificity and accuracy for the diagnosis of CAD and reflects functional cardiac information to some extent. It has

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Deterministic learning from sampling data

Cited by 36 publications

References 34 publications

Rapid dynamical pattern recognition for sampling sequences

Rapid dynamical pattern recognition for sampling sequences

Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

ECG‐based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease

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