A novel genetic algorithm with applications to the design of higher-order sigma-delta D/A converters
Bibliographic record
Abstract
The main fundamental problem in the existing genetic-algorithm-based techniques for the optimization of IIR digital filters stems from the fact that after the application of the constituent operations of crossover and mutation to the parent IIR digital filters, the resulting offspring IIR digital filters may cease to be BIBO stable. This paper presents a novel technique for the genetic optimization of IIR digital filters where the offspring IIR digital filters are guaranteed to be (inherently) BIBO stable. The resulting technique is particularly suitable for the optimization of the noise transfer function for /spl Sigma/-/spl Delta/ D/A converters. This is due to the fact that the classical IIR digital filter transfer function approximation techniques lead to the formation of delay-free loops in the D/A converter configuration proper. The usefulness of the proposed technique is demonstrated through its application to the design of a set of two 6-th order lowpass /spl Sigma/-/spl Delta/ D/A converters employing 64-times oversampling ratios for a final realization incorporating 8-bit multiplier coefficients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".