A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

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Small-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

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Small-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

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A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

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Large-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

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Parameter settings. by Olaide N. Oyelade (14047002)

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Fig 2 - by Olaide N. Oyelade (14047002)

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Structure of proposed HMMS. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Design variables. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Design parameters of proposed HMMS. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Comparison of 3-D FEA and experimental waveforms. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

LHS distribution of three parameters. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

WECs with MS replacing mechanical gearbox. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Comparison of 3-D FEA and experimental harmonics. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Flow chart of INFO-KELM model. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Sensitivity analysis results of HMMS. by Qiongfang Zhang (18055134)

“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…When considering noisy or stochastic expensive models, emulator GPBO finds the true parameters in 62% of the instances compared to approximately 0% for gradient-based nonlinear least-squares. We show that GPBO is more efficient than other popular derivative-free search algorithms, including genetic algorithms, the Nelder–Mead algorithm, or the simplicial homology global optimization algorithm. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…When considering noisy or stochastic expensive models, emulator GPBO finds the true parameters in 62% of the instances compared to approximately 0% for gradient-based nonlinear least-squares. We show that GPBO is more efficient than other popular derivative-free search algorithms, including genetic algorithms, the Nelder–Mead algorithm, or the simplicial homology global optimization algorithm. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…When considering noisy or stochastic expensive models, emulator GPBO finds the true parameters in 62% of the instances compared to approximately 0% for gradient-based nonlinear least-squares. We show that GPBO is more efficient than other popular derivative-free search algorithms, including genetic algorithms, the Nelder–Mead algorithm, or the simplicial homology global optimization algorithm. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…When considering noisy or stochastic expensive models, emulator GPBO finds the true parameters in 62% of the instances compared to approximately 0% for gradient-based nonlinear least-squares. We show that GPBO is more efficient than other popular derivative-free search algorithms, including genetic algorithms, the Nelder–Mead algorithm, or the simplicial homology global optimization algorithm. …”