Advanced Bayesian-Inspired Multilayer Effective Parameter Determination Method for Automated ANN Model Generation of Microwave Components
Bibliographic record
Abstract
Artificial neural networks (ANNs) have revolutionized microwave computer-aided design by leveraging machine-learning techniques to tackle complex problems. One critical aspect of this process is the automatic modeling of the neural network structure. The conventional approach, which involves qualitative adjustments to prevent underfitting and overfitting, often leads to inefficiencies when the initial structure significantly deviates from the optimal one. This article presents a groundbreaking approach to address this issue, proposing an automatic modeling algorithm for neural networks. This algorithm employs Bayesian theory to optimize the ANN model structure. Based on the Bayesian theory, the formula for calculating effective parameters in a multihidden layer neural network is derived, allowing the initial structure to adopt any form and permitting efficient, quantitative adjustments. This innovative approach enables the computation of effective parameters under any given structure. A higher maximum number of effective parameters in multihidden layer ANN has been obtained compared to single-hidden layer ANN, thus improving modeling accuracy. Compared with the existing Bayesian-based automated ANN model generation methods, the proposed approach significantly enhances both modeling accuracy and speed. The effectiveness of this method is verified through the application of three microwave components.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".