We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos and misspellings, common in almost any user-generated content, which hinder automatic te...
The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those...
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Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and...
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Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and multi-step least-squares algorithms, such as Weighted Null-Space Fitting. Recently, there has been an increasing interest in non-asymptotic analysis of estimation algorithms. In this contribution we identify the Markov parameters using weighted least-squares and present non-asymptotic analysis for such estimator. To cover both stable and unstable systems, multiple trajectories are collected. We show that with the optimal weighting matrix, weighted least-squares gives a tighter error bound than ordinary least-squares for the case of non-uniformly distributed measurement errors. Moreover, as the optimal weighting matrix depends on the system’s true parameters, we introduce two methods to consistently estimate the optimal weighting matrix, where the convergence rate of these estimates is also provided. Numerical experiments demonstrate improvements of weighted least-squares over ordinary least-squares in finite sample settings.
Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last ...
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Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them involve a common intermediate step, that is to estimate the range space of the extended observability matrix. In this contribution, an optimized version of the parallel and parsimonious SIM (PARSIM), PARSIM opt , is proposed by using weighted least-squares. It not only inherits all the benefits of PARSIM but also attains the best linear unbiased estimator for the above intermediate step. Furthermore, inspired by SIMs based on the predictor form, consistent estimates of the optimal weighting matrix for weighted least-squares are derived. Essential similarities, differences and simulated comparisons of some key SIMs related to our method are also presented.
A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy *** paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and ...
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A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy *** paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and privacy enforcement in such a ***,the authors discuss how HMM parameters and sensor measurements can be reconstructed from posterior distributions of an HMM ***,the authors consider a rational decision-maker that forms a private belief(posterior distribution)on the state of the world by filtering private *** authors show how to estimate such posterior distributions from observed optimal actions taken by the *** the setting of adversarial systems,the authors finally show how the decision-maker can protect its private belief by confusing the adversary using slightly sub-optimal *** range from financial portfolio investments to life science decision systems.
With the development of the semiconductor industry, the demand for wafer production has gradually increased. Wafer manufacturing is a very complicated process, and any abnormal fluctuations in each process in this pro...
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This paper investigates payload grasping from a moving platform using a hook-equipped aerial manipulator. First, a computationally efficient trajectory optimization based on complementarity constraints is proposed to ...
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With the dawn of e-Healthcare systems, Medical Record Management has become an important research problem. The storage and organization of medical records have made relatively little progress in a world of constantly ...
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Analysis of import substitution processes is one of the urgent problems for many countries in the context of deglobalization of the world economy. Mathematical methods of convex analysis make it possible to construct ...
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Artificial intelligence systems operating in the sequential decision making paradigm are inevitably required to do effective spatio-temporal processing. The memory models for such systems are often required not just t...
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