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Paper Citation Record · LEDGER

Nonlinear Systems Identification Using Deep Dynamic Neural Networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1610.01439.

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

pith.paper-citation-record.v1
1610.01439 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:26:01.346633Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:01:01.707148Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8f5e0b0c-d3c0-46d7-89a8-50e8a063122e · inbound

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics cites this paper.

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics Nonlinear Systems Identification Using Deep Dynamic Neural Networks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-14T14:26:01.346633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:26:01.346633Z digest=sha256:1a9ab98883e66d271f0bc0fc663291f621b1b46279fb25c1a245aa97a660b46b

Observation edaf7e9f-4ae9-44ce-87a9-735d78420cce · inbound

Deep learning-based pavement performance modeling using multiple distress indicators and road work history cites this paper.

Deep learning-based pavement performance modeling using multiple distress indicators and road work history Nonlinear Systems Identification Using Deep Dynamic Neural Networks

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-07-04T21:27:19.891292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T15:41:59.201544Z digest=sha256:ea234e06f10a121f29e0b0b9db0991d629295594af109d673399011424a432c8