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Record W2159972742 · doi:10.1109/icnn.1993.298648

Dynamic neural unit and function approximation

2002· article· en· W2159972742 on OpenAlexaff
D.H. Rao, Manas Gupta

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.>

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2002
Admission routes1
Has abstractyes

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