THE BIAS TEMPERATURE DEPENDENCE ESTIMATION AND COMPENSATION FOR AN ACCELEROMETER BY USE OF THE NEURO-FUZZY TECHNIQUES
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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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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 it