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Record W2154740766 · doi:10.1109/icassp.2003.1201769

Blind identifiability of third-order Volterra nonlinear systems

2004· article· en· W2154740766 on OpenAlexaff
Hong‐Zhou Tan, T. Aboulnasr

Bibliographic record

Venue2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). · 2004
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIdentifiabilityUnobservableNonlinear systemKernel (algebra)Volterra seriesMathematicsComputer scienceHigher-order statisticsApplied mathematicsKernel density estimationControl theory (sociology)AlgorithmMathematical optimizationSignal processingStatisticsArtificial intelligenceEconometricsTelecommunications

Abstract

fetched live from OpenAlex

A novel approach to estimate blindly the kernels of a Volterra nonlinear system up to the third order is proposed. The system is excited by an unobservable i.i.d. random sequence. Blind identifiability is achieved using second order statistics (SOS) rather than using higher order statistical (HOS) information to ensure lower complexity. Since the output of the Volterra system is linearly dependent upon its kernel parameters, conventional LMS or RLS algorithms can be used and consistent estimation of Volterra kernels can be achieved provided some conditions of persistent excitation (PE) are satisfied. Simulation demonstrated the ability of the proposed method to achieve a good estimation performance.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.310
Teacher spread0.264 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2004
Admission routes1
Has abstractyes

Explore more

Same venue2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).Same topicBlind Source Separation TechniquesFrench-language works237,207