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M5′ algorithm was employed for building and evaluating model trees.
Finally, the support vector machine multi-classification algorithm was employed for load recognition.
Back propagation algorithm was employed for the ANN training; a Tansig function was used as the nonlinear transfer function.
Back Propagation (BP) algorithm was employed for the ANN training in which a Tansig function was used as the nonlinear transfer function.
A Maximum Entropy (MaxEnt) algorithm was employed for estimating habitat suitability for the different activities of each species based on the afore-mentioned variables.
The SIMPLE algorithm was employed for pressure velocity coupling.
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The RANSAC algorithm is employed for outlier detection.
Subsequently, the extended Kalman filter algorithm is employed for model based state-of-power prediction.
The linear quadratic Gaussian (LQG) algorithm is employed for the controller design.
In addition, a B-dot algorithm is employed for the detumbling before the boom is deployed.
The linear quadratic Gaussian (LQG) algorithm is employed for controller design.
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