Multilayer hybrid fuzzy neural networks: synthesis via technologies of advanced computational intelligence
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we develop an advanced architecture and come up with a comprehensive design methodology of genetically optimized hybrid fuzzy neural networks (gHFNNs). The construction of gHFNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNN results from a highly synergistic usage of the genetic optimization-driven hybrid system being generated by combining fuzzy neural networks (FNNs) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. The optimization of the FNN is realized with the aid of a standard backpropagation learning algorithm and genetic optimization. As the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method-based learning (optimization). Through the consecutive process of such structural and parametric optimization, an optimized PNN becomes generated in a dynamic fashion. To evaluate the performance of the gHFNNs, we experimented with a number of representative numerical examples. A comparative analysis demonstrates that the proposed gHFNNs are neurofuzzy systems with higher accuracy as well as more superb predictive capability than other models available in the literature
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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