An Experimental Investigation on Feature Extraction and Fault Detection in Analog Circuits Using Fuzzy Logic and Neural Network
Bibliographic record
Abstract
This paper introduces an innovative approach to enhance feature selection and enable dual fault diagnosis in analog circuits.By harnessing the combined power of fuzzy logic and neural networks, this study presents a robust framework for precisely pinpointing faults within the circuit.The operational integrity of any circuit hinges upon its inherent parameters, and the presence of defective components within the circuit distorts its performance, resulting in output deviations.The techniques elucidated in this research not only identify the specific faulty component but also quantify the extent of its deviation from the original parameters.A comparative analysis between the efficacy of fuzzy logic and neural networks in addressing this challenge is expounded upon.To demonstrate the practical application of these methodologies, a Sallen-key Bandpass filter is employed as the circuit under scrutiny (CUT).A comprehensive fault dictionary, constructed via the extraction of pertinent features from the CUT, serves as the cornerstone of this study.Notably, the fault table is meticulously generated, utilizing a step size of +/-5% for parameter value perturbation.
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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".