A Comparative Study of Multi-Layer Perceptron and Jordan Recurrent Neural Networks for Signals Classification in a Robotic System
Bibliographic record
Abstract
The artificial neural network (ANN) technique, one of the most sophisticated and advanced technologies of the present day, is leveraged across various fields due to its precision and superior performance.One such field that significantly relies on ANN is the detection of errors and classification of signals, especially in non-linear dynamic systems such as robotic systems.Mechanical and electrical faults at the robot's joints often manifest as disturbances, causing vibration at the robot arm's tip.This paper explores the offline detection and diagnosis of these tip acceleration values using eight levels of discrete wavelet transform (DWT).The output from the DWT serves as the input for the signal classification process.A comparative analysis between two neural network types, multilayer perceptron and the Jordan recurrent neural network, is undertaken to achieve signal classification.Signals from various non-healthy scenarios, along with the healthy case, are utilized to train the neural networks.The errors under consideration occur either individually at the robot's joints or at different joints simultaneously.The results indicate that, when applied to the same set of offline recorded data, the multi-layer perceptron neural network demonstrates 100% accuracy, while the Jordan recurrent neural network achieves 97% accuracy in data classification.Thus, for error classification in the robot arm, the multi-layer perceptron neural network outperforms the Jordan neural network.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".