322,359 research outputs found
Geometric characterization on the solvability of regulator equations
The solvability of the regulator equation for a general nonlinear system is discussed in this paper by using geometric method. The ‘feedback’ part of the regulator equation, that is, the feasible controllers for the regulator equation, is studied thoroughly. The concepts of minimal output zeroing control invariant submanifold and left invertibility are introduced to find all the possible controllers for the regulator equation under the condition of left invertibility. Useful results, such as a necessary condition for the output regulation problem and some properties of friend sets of controlled invariant manifolds, are also obtained
Non-linear Learning for Statistical Machine Translation
Modern statistical machine translation (SMT) systems usually use a linear
combination of features to model the quality of each translation hypothesis.
The linear combination assumes that all the features are in a linear
relationship and constrains that each feature interacts with the rest features
in an linear manner, which might limit the expressive power of the model and
lead to a under-fit model on the current data. In this paper, we propose a
non-linear modeling for the quality of translation hypotheses based on neural
networks, which allows more complex interaction between features. A learning
framework is presented for training the non-linear models. We also discuss
possible heuristics in designing the network structure which may improve the
non-linear learning performance. Experimental results show that with the basic
features of a hierarchical phrase-based machine translation system, our method
produce translations that are better than a linear model.Comment: submitted to a conferenc
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