Nonlinear System Identification using Genetic Algorithm Based Recurrent Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new genetic algorithm (GA) is developed to optimize the architecture of a recurrent artificial neural network (RANN) with multiple hidden layers. A new direct matrix mapping encoding (DMME) method is proposed to efficiently and effectively represent the architecture of a neural network. A modified back-propagation (BP) algorithm is utilized to tune the weights and other parameters of RANNs. The RANN optimized by this algorithm has been applied to the identification of nonlinear dynamic systems with unknown nonlinearities. Three types of RANN-based nonlinear models are proposed to describe the behavior of nonlinear systems. The effectiveness of these models and identification algorithms are extensively verified in the identification of several complex nonlinear systems such as "smart" actuator preceded by hysteresis, and friction-plague harmonic drive
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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