Prediction error
A separable prediction error method for robot identification
... **** Université de Nantes, IRCCyN, 1, rue de la Noë, BP 92101, 44321 NANTES Cedex 3, France e-mail: Maxime.Gautier@irccyn.ec-nantes.fr Abstract: The Prediction Error Method, developed in[r] ...
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Data Informativity for the Open-Loop Identification of MIMO Systems in the Prediction Error Framework
... In Prediction Error identification, to obtain a consistent estimate of the true system, it is crucial that the input excitation yields informative data with respect to the chosen model ...in ...
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A separable prediction error method for robot identification
... **** Université de Nantes, IRCCyN, 1, rue de la Noë, BP 92101, 44321 NANTES Cedex 3, France e-mail: Maxime.Gautier@irccyn.ec-nantes.fr Abstract: The Prediction Error Method, developed in[r] ...
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Prior Precision Modulates the Minimization of Auditory Prediction Error
... inducing prediction error: the unpredicted condition (where there is no precise prediction) and the mispredicted condition (where there is a precise prediction being ...with prediction ...
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Bayesian Support Vector Regression for traffic speed prediction with error bars
... BSVR error bars. In this analysis, we expect large prediction error, if the corresponding error bar value is above its ...multiple prediction horizons for expressway ...small ...
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Model-free spatial Interpolation and error prediction for survey data acquired by mobile platforms
... based estimators of C ise are sensitive to the correctness of the assumed models, as our numerical results below show. A method widely used by practitioners to chose algorithm’s parameters is based on Cross Validation ...
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Correcting the impact of docking pose generation error on binding affinity prediction
... generation error, with reasonably accurate prediction still being obtained in poses with RMSD of almost ...generation error generally correlates with the binding affinity prediction ...
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Comments on "bounded error adaptive control"
... The presence of persistent errors, coupled with the adaptive gain mechanism of MRAC algorithms, cause "drifts" in the adaptive gain parameters, which in turn increas[r] ...
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Revisiting Value Prediction
... 1 Introduction Around 2020, IC technology might allow to build processor chips integrating 50-100 superscalar cores. Intrinsically parallel workloads strongly benefit from additional cores. Yet the software industry is ...
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CONVERGENT ERROR-CONTROLLED MESH ADAPTATION
... where we have introduced the discrete extension of the interpolation error. It is then reasonable to try to minimize the RHS of this inequality instead of the LHS. However, this still involves some difficulty due ...
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Error structures and parameter estimation
... an error structure built from the parametric model allows to propagate the accuracy through calculations performed with the parameter thanks to a coherent specific differential calculus (property 1) of ...Moreover ...
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Vortex Exergy Prediction
... Firstly, the axial velocity field shown in Fig.2, where the typical flow phenomena is observed: a stagnation point at the leading edge, a curvature-based acceleration region and [r] ...
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ACI error survey: Canadian data
... / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur.. For the publisher[r] ...
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Stabilizing Minimum Error Rate Training
... The most commonly used method for training feature weights in statistical ma- chine translation (SMT) systems is Och’s minimum error rate training (MERT) pro- cedure.. A well-known probl[r] ...
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Human error and structural practice
... / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur.. Access and use of[r] ...
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Channel-adapted quantum error correction
... Since the average entanglement fidelity is linear in the channel and CPTP maps correspond to positive semidefinite operators, we can determine the op- timal channel-ada[r] ...
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Pose Error Effects on Range Sensing
... simple error model based on Gaussian statistics, as previously ...Pose error is also dicult to character- ize and quantify in ...pose error eect has been studied in isolation. Multiple types of ...
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Student Dropout Prediction
... tor Machine (SVM) [3] and Random Forest (RF), as they are the most commonly used models in literature to solve similar problems. LDA acts as a dimensional reduction algorithm, trying to reduce the data complexity, i.e. ...
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Output error methods for robot identification
... min and l CLOE max . It comes out that the CLIE method is more important at the input than at the output. That is confirmed by the singular values of the Jacobian matrices in Table 2 . The interest of considering the ...
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Error Propagations for Local Bundle Adjustment
... respectively. Although this complexity benefits the sparse structure of the problem and the assumption c ≪ p, real- time GBA is impossible for long sequences. Error Propagation Error propagation provides ...
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