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Record W2159972742 · doi:10.1109/icnn.1993.298648

Dynamic neural unit and function approximation

2002· article· en· W2159972742 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE International Conference on Neural Networks · 2002
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceActivation functionArtificial neural networkTrigonometric functionsFunction approximationFeed forwardFunction (biology)Feedforward neural networkNonlinear systemRoboticsArtificial intelligenceControl theory (sociology)TrigonometryTask (project management)AlgorithmControl engineeringMathematicsControl (management)RobotEngineeringPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

A dynamic model of a neuron for applications in robotics and control is developed. The proposed model, called the dynamic neural unit (DNU), comprises two distinct operations: the synaptic operation, which determines the optimum feedforward and feedback synaptic weights controlling the dynamics of the neuron; and the somatic operation, which determines the optimum gain of the nonlinear activation function for a given task. The function approximation capability of the network of DNUs is described by considering linear and trigonometric operators. Computer simulation results are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.278
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it