A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

Guerra, Juan F.; Garcia-Hernandez, Ramon; Llama, Miguel A.; Santibañez, Victor

doi:10.3390/a16080393

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…For example, it has been demonstrated that there are important contributions in modeling the steel connections of seismic performance used in civil engineering with artificial intelligence [29][30][31]. As another example, Guerra et al [32] performed a comparative study of swarm intelligence metaheuristics in the unscented Kalman filter-based neural training applied to the identification and control of robotic manipulators. Islam et al [33] conducted a study related to convolutional neural networks based on transfer learning models using data augmentation and transformation for the detection of concrete cracks.…”

Section: Modelmentioning

confidence: 99%

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Dis

2023

Sustainability

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The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (PBias = −1.65%, MAE = 1.27 mm d−1, RMSD = 1.6 mm d−1, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, PBias = −6.44%, MAE = 3.58 mm d−1, RMSD = 4.2 mm d−1, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques.

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Section: Modelmentioning

confidence: 99%

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Dis

2023

Sustainability

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“…The relative performance of different swarm intelligence algorithms is not always understood; however, there is burgeoning work in this area. For instance, Guerra et al ( 2023) compare how four swarm algorithms perform in optimizing unscented Kalman filter parameters in a robotics application [37]. We refer those interested in algorithm performance comparisons in technical applications or dimension reduction to the following work [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Search on an NK Landscape with Swarm Intelligence: Limitations and Future Research Opportunities

Chen,

Miller,

Toh

2023

Algorithms

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Swarm intelligence has promising applications for firm search and decision-choice problems and is particularly well suited for examining how other firms influence the focal firm’s search. To evaluate search performance, researchers examining firm search through simulation models typically build a performance landscape. The NK model is the leading tool used for this purpose in the management science literature. We assess the usefulness of the NK landscape for simulated swarm search. We find that the strength of the swarm model for examining firm search and decision-choice problems—the ability to model the influence of other firms on the focal firm—is limited to the NK landscape. Researchers will need alternative ways to create a performance landscape in order to use our full swarm model in simulations. We also identify multiple opportunities—endogenous landscapes, agent-specific landscapes, incomplete information, and costly movements—that future researchers can include in landscape development to gain the maximum insights from swarm-based firm search simulations.

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A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

Cited by 2 publications

References 46 publications

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Search on an NK Landscape with Swarm Intelligence: Limitations and Future Research Opportunities

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