Swarm Intelligence-Homotopy Hybrid Optimization-Based ANN Model for Tunable Bandpass Filter
Bibliographic record
Abstract
High-performance tunable radio frequency (RF)/ microwave and millimeter-wave filter design is a challenging task due to the lack of a basic theory. The filtering characteristics are highly sensitive to the variation of tuning elements that are commonly modeled and achieved by optimization algorithms. However, those optimizations only provide satisfactory results with a good set of initial parameters. Such range-limited optimization algorithms generally have issues of falling into local optima, slow convergence, and cumbersome implementation. To mitigate this problem, for the first time, a topology-based local optimizer is integrated with metaheuristic global optimization algorithms in this work. We have hybridized the homotopy method with an improved whale optimization algorithm (WOA) and a gray wolf optimization (GWO) algorithm. In this work, an artificial neural network (ANN) is formulated and studied, which has twofold applications. First, ANN is used as a surrogate model to represent the time-consuming electromagnetic (EM) model in expediting the hybrid optimization process of tunable filters. Second, an ANN model is developed on data generated by the proposed optimization algorithm for predicting tunable circuit parameters at different tuning stages. The proposed ANN model-based algorithm is then applied to a fifth-order lumped-element tunable circuit and two fourth-order full-wave EM simulation models of two tunable bandpass filters (tBPFs). The calculated results out of the ANN model demonstrate a good agreement with simulation and measurement counterparts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".