Application of Diversity Controlled Genetic Algorithms to the Design and Optimization of OTA-C IF Filters
Bibliographic record
Abstract
IF filters find diverse practical applications in modern communication systems. In the current analog IC fabrication technologies, IF filters are usually implemented as OTA-C filters. The design of OTA-C IF filters can be achieved by employing either the existing gradient-based optimization techniques or the conventional genetic algorithms (GAs). However, the latter approach may result in relatively slower convergence speeds. This paper presents a novel application of diversity controlled (DC) GAs to the rapid optimization of OTA-C IF filters. In this application, the gains associated with the constituent OTA transconductances are used as optimization parameters. DCGA is well known to exhibit an order of magnitude improvement in the convergence speed as compared to conventional GAs. The proposed approach is illustrated through its application to the optimization of an OTA-C IF filter consisting of 39 OTAs for operation around a center frequency of 455 kHz.
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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".