Identification of a dynamic model for shape memory alloy actuator using Hammerstein-Wiener gray box and mutable smart bee algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators. Design/methodology/approach – To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier. Findings – Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator. Through different experiments, the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators. Furthermore, by extending the conclusions and expounding the obtained results, one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions. Based on the exerted experiments and implementing the method, the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks. Additionally, the derived gray box method has a higher degree of preference over the black box identifiers, as it allows a manipulated expert to extract the knowledge of the system at hand. Originality/value – The originality of the research paper is twofold. From the practical (engineering) point of view, the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model. From the computational point of view, the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand, and high computational expense pertinent to the structure of black-box identifiers.
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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 | 0.001 | 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