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The fuzzy controller is optimized using a least squares error criterion.
The fuzzy controller is optimized using a least squares error criterion and, as a result, a higher-order adaptive controller is achieved.
Essentially, the position of the contributing sources was assumed to be known and a least squares error procedure was employed to establish an optimum fit between the source strength distribution and the cross-spectral data.
In the conventional eigenfilter technique a reference frequency-angle point is required, whereas in the eigenfilter technique based on a TLS (total least squares) error criterion, no reference point is required.
Higher-order accuracy near the immersed boundary is achieved by combining the ghost-cell approach with a weighted least squares error method based on a higher-order approximate polynomial.
When the system determines that the process has generated a large change, the system applies the design-of-experiment concept continuously to collect process data over an expanded experimental range and uses these data with the least squares error (LSE) method to estimate new model parameters.
Similar(19)
Instead of fitting the boundary conditions using collocation points, we minimize the error of fit in the l2 norm to minimize the least-squares error.
However, such an unavoidable regularization procedure increases the least-squares error, which also depends on the position of the secondary source.
The robust solution of the developed sensitivity equation is achieved through a least-squares error minimization scheme, and the challenging issues are discussed.
I suggest that optimization by means of general minimization techniques needs to be supplemented by a theoretical understanding of the SFM least-squares error surface.
The least-squares error method, variational method, Gauss Seidel method and Newton Raphson method are employed to calculate the optimum shape of slider bearing.
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