THE BIAS TEMPERATURE DEPENDENCE ESTIMATION AND COMPENSATION FOR AN ACCELEROMETER BY USE OF THE NEURO-FUZZY TECHNIQUES
Bibliographic record
Abstract
In this paper, we describe a new method for improved performance of inertial sensors, with applications in strap-down inertial systems. A new empirical model is proposed for the bias temperature dependence compensation of accelerometers using their input and output data. Experimental testing of the accelerometer is first realized, as data for 2 inputs and 1 output are collected. Based on this data, an empirical model is built using a neuro-fuzzy network, which learns the process behavior and uses a Fuzzy Inference System (FIS) for model realization. The improvement in the reproduction quality of the experimental surface by the neuro-fuzzy model is achieved through the FIS training using a Sugeno learning algorithm with two inputs and one output. Generation and training of the FIS are performed with Matlab functions, the training of which is realized on a high number of epochs, for example, on a number of 10 5 training epochs. It is noticed that the proposed algorithm leads to a 35.5 times reduction in the error due to temperature dependence of the bias.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".