Paper Citation Record · LEDGER

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach

As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2505.10678.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.10678 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:12:23.984851Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:18:53.407667Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T18:18:55.195505Z

Reference resolution

47 of 47 outbound references displayed

verified exact4
verified fuzzy30
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac45c54a-9710-420d-bc71-9d435e463bf5 · outbound

This paper cites Lyapunov-based real-time and iterative adjustment of deep neural networks,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Lyapunov-based real-time and iterative adjustment of deep neural networks,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.790546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab409898-ee28-4b04-83d7-a7d3f7da3fef · outbound

This paper cites Design and flight evaluation of deep model reference adaptive controller,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Design and flight evaluation of deep model reference adaptive controller,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.762314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4dd34977-9446-46c9-a1ca-dd6d8a7cf44f · outbound

This paper cites Lyapunov-derived control and adaptive update laws for inner and outer layer weights of a deep neural network,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Lyapunov-derived control and adaptive update laws for inner and outer layer weights of a deep neural network,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.748802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d82e359-3322-407c-8723-da311f97e2a9 · outbound

This paper cites Composite adaptive Lyapunov-based deep neural network (Lb-DNN) controller,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Composite adaptive Lyapunov-based deep neural network (Lb-DNN) controller,

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-15T21:12:24.237767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 02583633-e97e-4017-98c1-32cbc3d67cb9 · outbound

This paper cites Adaptive Deep Neural Network-Based Control Barrier Functions.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Adaptive Deep Neural Network-Based Control Barrier Functions

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:12:24.095861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c131e161-98f9-40b8-b2f4-9aa5409ff19e · outbound

This paper cites Augmentation of a Lyapunov-based deep neural network controller with concurrent learning for control-affine nonlinear systems,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Augmentation of a Lyapunov-based deep neural network controller with concurrent learning for control-affine nonlinear systems,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.734083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29321a6f-cbe4-4d4d-bfa5-191ded57f3a8 · outbound

This paper cites Lyapunov-based physics-informed long short-term memory (LSTM) neural network- based adaptive control,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Lyapunov-based physics-informed long short-term memory (LSTM) neural network- based adaptive control,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.720684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f891acdd-c4dc-4efa-8a1c-39d3f31d4147 · outbound

This paper cites Lyapunov-based long short-term memory (Lb-LSTM) neural network-based control,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Lyapunov-based long short-term memory (Lb-LSTM) neural network-based control,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.824235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e18ba2be-af95-475f-a894-a1c954b5f3d0 · outbound

This paper cites Neural lander: Stable drone landing control using learned dynamics,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Neural lander: Stable drone landing control using learned dynamics,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.696127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82bb3f5f-ab3d-4200-a6be-b63686f130da · outbound

This paper cites Deep learning helicopter dynamics models,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep learning helicopter dynamics models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.681992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bfd6ea7e-4d7f-46c2-acf0-aeab354b182b · outbound

This paper cites Learning quadrotor dynamics using neural network for flight control,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Learning quadrotor dynamics using neural network for flight control,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.667695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65ac1870-9ca3-46af-ab29-466561db02a0 · outbound

This paper cites Deep neural networks for improved, impromptu trajectory tracking of quadrotors,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep neural networks for improved, impromptu trajectory tracking of quadrotors,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.654342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c571088d-be15-4db6-8c24-f3b0d68467cb · outbound

This paper cites Efficient representation and approximation of model predictive control laws via deep learning,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Efficient representation and approximation of model predictive control laws via deep learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.640406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a6ecbe7-999f-4229-8624-d152adb1684b · outbound

This paper cites Deep Lyapunov-based physics-informed neural networks (DeLb-PINN) for adaptive control design,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep Lyapunov-based physics-informed neural networks (DeLb-PINN) for adaptive control design,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.848493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68cf8346-0bc5-40d2-989a-d936f9a823d4 · outbound

This paper cites Accelerated gra- dient approach for neural network-based adaptive control of nonlinear systems,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Accelerated gra- dient approach for neural network-based adaptive control of nonlinear systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.617739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 89575099-5a1c-4797-8435-daeaee0f0d5f · outbound

This paper cites Deep adaptive indirect herding of multiple target agents with unknown interaction dynamics,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep adaptive indirect herding of multiple target agents with unknown interaction dynamics,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.603651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 348ad494-5d56-428b-baf4-6ec8661dc493 · outbound

This paper cites Adaptive deep neural network optimized control for a class of nonlinear strict-feedback systems with prescribed performance,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Adaptive deep neural network optimized control for a class of nonlinear strict-feedback systems with prescribed performance,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.590342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d62a1fc1-0e45-48bf-8389-8bc692fe6480 · outbound

This paper cites Deep neural networks- based output-dependent intermittent control for a class of uncertain nonlinear systems,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep neural networks- based output-dependent intermittent control for a class of uncertain nonlinear systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.576775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ee50e38-6962-4447-92f1-499e597dc33c · outbound

This paper cites Uncertainty-Aware Guidance for Target Tracking subject to Intermittent Measurements using Motion Model Learning.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Uncertainty-Aware Guidance for Target Tracking subject to Intermittent Measurements using Motion Model Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:12:24.076232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20051f58-294a-4b55-8ba7-38ba0312e32b · outbound

This paper cites Composite learning robot control with guaranteed parrameter convergence,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Composite learning robot control with guaranteed parrameter convergence,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.562397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87b98e7c-aaf2-43fa-af24-16a4eef2d4c3 · outbound

This paper cites Concurrent learning adaptive model predictive control,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Concurrent learning adaptive model predictive control,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.547431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c2267909-855c-4eb6-831d-9b0a1bea986d · outbound

This paper cites Integral concurrent learning: Adaptive control with parameter convergence using finite excitation,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Integral concurrent learning: Adaptive control with parameter convergence using finite excitation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.532377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d2c5d39b-3c1e-4ce9-8d57-5031ea3f8527 · outbound

This paper cites Concur- rent learning for parameter estimation using dynamic state-derivative estimators,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Concur- rent learning for parameter estimation using dynamic state-derivative estimators,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.515636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a01f29f9-c848-4129-b78c-b307c3a6deec · outbound

This paper cites Combined MRAC for unknown MIMO LTI systems with parameter convergence,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Combined MRAC for unknown MIMO LTI systems with parameter convergence,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.501711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f62a817e-6b59-49c8-b738-84e9c3f76266 · outbound

This paper cites New results on parameter estimation via dynamic regressor extension and mixing: Continuous and discrete-time cases,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach New results on parameter estimation via dynamic regressor extension and mixing: Continuous and discrete-time cases,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.487992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93e0a7b2-d912-47eb-9278-19350884885e · outbound

This paper cites Parameter convergence in nonlinearly parameterized systems,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Parameter convergence in nonlinearly parameterized systems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.474839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f065761f-73fb-4483-9cc1-504ded7eabed · outbound

This paper cites Identifiability Implies Robust, Globally Exponentially Convergent On-line Parameter Estimation: Application to Model Reference Adaptive Control.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Identifiability Implies Robust, Globally Exponentially Convergent On-line Parameter Estimation: Application to Model Reference Adaptive Control

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:12:24.056481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 422df507-57e0-47e8-820b-dc3c338e4525 · outbound

This paper cites Parameter estimation of two classes of nonlinear systems with non-separable nonlinear parameterizations,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Parameter estimation of two classes of nonlinear systems with non-separable nonlinear parameterizations,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.461498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2339d825-aa61-4953-8889-e263b36b1737 · outbound

This paper cites The power of deeper networks for expressing natural functions,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach The power of deeper networks for expressing natural functions,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.910856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7215f97b-f0e9-4001-ad75-3d8ed9d61c8e · outbound

This paper cites Deep residual neural network (ResNet)-based adaptive control: A Lyapunov-based approach,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep residual neural network (ResNet)-based adaptive control: A Lyapunov-based approach,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.914897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7dbb2f8-e5a1-4751-b270-4e9ad89c1a31 · outbound

This paper cites Universal approximation with deep narrow networks,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Universal approximation with deep narrow networks,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.918992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d932bf-bc15-42b9-bf50-33bd161a781d · outbound

This paper cites Goodfellow, Y.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Goodfellow, Y

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.417606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f5c9a4b-6172-42e9-a86e-1afc2c76670c · outbound

This paper cites Depth with nonlinearity creates no bad local minima in ResNets,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Depth with nonlinearity creates no bad local minima in ResNets,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.402851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f39a88fb-a34d-4b9a-81e6-36f288c8baa1 · outbound

This paper cites Deep learning without poor local minima,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Deep learning without poor local minima,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.387710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a0e64f29-3e2b-4dc6-93ce-08848f8ca471 · outbound

This paper cites Depth Creates No Bad Local Minima.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Depth Creates No Bad Local Minima

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.935495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81d6c646-e192-46f2-80be-717d289ba04f · outbound

This paper cites Gradient descent learns one-hidden-layer CNN: Dont be afraid of spurious local min- ima,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Gradient descent learns one-hidden-layer CNN: Dont be afraid of spurious local min- ima,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.374107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9d67be65-37f5-43f4-9b8c-e5cfb59ca646 · outbound

This paper cites Identifiability of linear and nonlinear dynamical systems,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Identifiability of linear and nonlinear dynamical systems,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.358365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f86822cb-cbb6-49b9-932b-7d3590ef6a77 · outbound

This paper cites Boyd and L.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Boyd and L

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.948550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 255dfd03-a72c-4ba5-addf-dfa1b20ef451 · outbound

This paper cites Friedberg, A.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Friedberg, A

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.335001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40ec87e9-8cb6-427d-ae09-133d50e562c5 · outbound

This paper cites Krstic, I.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Krstic, I

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.956685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e1b7457-0129-47ad-b14c-9798aa782e1a · outbound

This paper cites Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.960758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312bed24-c74b-4e6c-a21b-8372d4673e71 · outbound

This paper cites Ioannou and J.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Ioannou and J

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.965151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c117425-33b8-4608-9be5-d1a57571ac76 · outbound

This paper cites A new least squares parameter estimator for nonlinear regression equations with relaxed ex- citation conditions and forgetting factor,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach A new least squares parameter estimator for nonlinear regression equations with relaxed ex- citation conditions and forgetting factor,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.302128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9dc9ff99-0701-4729-87d1-ddf087eb9eb9 · outbound

This paper cites an unresolved cited work.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Unresolved cited work

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:23.973380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3d675e3-3567-400e-a4b3-0880dfe02cba · outbound

This paper cites Composite adaptive control of robot manip- ulators,.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Composite adaptive control of robot manip- ulators,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.278508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30e471f7-92a5-4065-bd8b-0086538b1d81 · outbound

This paper cites an unresolved cited work.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:12:24.265922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c1e76c1-a111-44b5-884c-35e91926ba9c · outbound

This paper cites Her research interests include Lyapunov-based control techniques, deep learning methods, and physics-informed learning.

System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Her research interests include Lyapunov-based control techniques, deep learning methods, and physics-informed learning

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:24.251785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 7d81d68e-d2cb-412e-9064-90efda99efe5 · inbound

LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller cites this paper.

LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:18:55.348785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.