MétaCan
Menu
Back to cohort
Record W2143804978 · doi:10.1109/iscas.1989.100702

Nonlinear IIR adaptive filtering using a bilinear structure

2003· article· en· W2143804978 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInfinite impulse responseAdaptive filterFinite impulse responseNonlinear systemControl theory (sociology)Bilinear interpolationLinear filterComputer scienceMathematicsVolterra seriesBilinear transform2D FiltersFilter (signal processing)Digital filterAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

A nonlinear IIR (infinite-impulse-response) adaptive filter that is based on a Volterra polynomial realized by a set of bilinear systems is presented. Each bilinear system corresponds to a cascade connection of linear systems and multipliers. This allows the extension of some linear adaptive concepts. Nonlinear IIR adaptive filters have an advantage over FIR (finite-impulse-response) Volterra filters in that in some applications they may result in less computation. The results of a simulation example show the applicability and practical convergence rate of the nonlinear IIR adaptive filter 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.264
Threshold uncertainty score0.984

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 communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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

Quick stats

Citations16
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Adaptive Filtering TechniquesFrench-language works237,207