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A training scheme for the fuzzy controller is derived that minimizes the output error between a reference model and the plant.
A numerical example shows that the filtering error systems will work well against bounded error between a nonlinear dynamical system and a multilayer neural network.
The second approach consists of a numerical optimization procedure that identifies material parameters by reducing the error between a finite element model and the experimental load deflection results.
The initial localized generalization error model (LGEM) aims to find an upper bound of error between a target function and a radial basis function neural network (RBFNN) within a neighborhood of the training samples.
The methodology is implemented to solve three practical design problems; the first test case considers a reconstruction design optimization that minimizes the pressure error between a predefined pressure curve and candidate pressure distribution.
In our method, both inner and boundary vertices are repositioned by analytically minimizing the L1 error between a paraboloid function and its piecewise linear interpolation over the neighborhood of each vertex.
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The values are comparable, within statistical error, between Group A and Group B at week 4 and week 8, and relative to their respective baseline values.
The error between an observed signal and its model is passed as the input to the decision-making (detection) module.
A common method for real-time segmentation of moving regions in image sequences involves ''background subtraction," or thresholding the error between an estimate of the image without moving objects and the current image.
Formally, the error between an estimated 3D joint and the truly performed one from ground truth data is computed as.
The difference in classification error between an sRSM with pre-trained and randomly initialized weights on USC IEMOCAP.
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