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The second unique characteristic of SVM is that it is established on the unique theory of the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization error and therefore very resistant to the overfitting problem, eventually achieving a high generalization performance.
The optimal controller is achieved by minimizing an upper bound on H2 performance.
These methods are based on minimizing an upper bound of the sum of the block-coherences of the sensing matrix blocks.
This solution guarantees a prescribed estimation level of accuracy while minimizing an upper bound on the covariance of the estimation error.
This problem is solved by minimizing an upper bound for the error resulting from the truncation of the kernel expansion into a finite number of functions.
Furthermore, by utilizing the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of H2 performance.
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We, then, theoretically prove that the minimization of the above function acts to minimize an upper bound of the network's square error.
An LPV filter, which minimizes an upper bound to the H∞ performance of the estimation error, is obtained as the solution of an optimization problem.
To demonstrate the potential of the proposed algorithm a method for designing fixed-structure controllers that minimize an upper bound on the singular value µ is presented.
Moreover, the conditions for the existence of a fuzzy H∞ observer that minimizes an upper bound to H∞ norms are also proposed.
Specifically, an expression for the expected estimate variance is derived, and a method for manufacturing an input sequence that minimizes an upper bound on this variance is developed, which depends on the solution to a convex optimization problem.
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