Titlebook: Dynamical Systems, Control, Coding, Computer Vision; New Trends, Interfac Giorgio Picci,David S. Gilliam Conference proceedings 1999 Birkhä

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造反,叛乱

https://doi.org/10.1007/978-3-663-11814-5cation to probabilistic robustness analysis. Subsequently, we explain how probabilistic robust design can be performed and we discuss connections with Learning Theory, showing how the problem structure can be taken into account. Current research directions related to sample generation in various sets are finally outlined.

或者发神韵

Probabilistic Robustness Analysis and Design of Uncertain Systems,cation to probabilistic robustness analysis. Subsequently, we explain how probabilistic robust design can be performed and we discuss connections with Learning Theory, showing how the problem structure can be taken into account. Current research directions related to sample generation in various sets are finally outlined.

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State Space Methods for Analysis Problems Involving Rational Matrix Functions, of solving explicitly integral equations of convolution type, direct and inverse spectral problems for canonical systems of differential equations, and with the problem of constructing exact solutions of nonlinear partial differential equations.

NOVA

Conference proceedings 1999interaction of these disciplines with new sectors of engineering and applied sciences like coding, computer vision, and hybrid systems. These are new areas of rapid growth and of increasing importance in modern technology. The essays, written by world-leading experts in the field, reproduce and expa

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Nonlinear Feedback Stabilization Revisited,e seen as an updated version of [22], discusses the problem of state stabilization, the understanding of which is a fundamental prerequisite to the solution of control problems such as tracking, disturbance rejection, output feedback, or adaptive and robust design.

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