The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
David E. Orin - One of the best experts on this subject based on the ideXlab platform.
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efficient o n Recursive Computation of the operational space inertia matrix
Systems Man and Cybernetics, 1993Co-Authors: Kathryn W. Lilly, David E. OrinAbstract:The operational space inertia matrix Lambda reflects the dynamic properties of a robot manipulator to its tip. In the control domain, it may be used to decouple force and/or motion control about the manipulator workspace axes. The matrix Lambda also plays an important role in the development of efficient algorithms for the dynamic simulation of closed-chain robotic mechanisms, such as multiple manipulator systems and walking machines. This paper presents the development of a Recursive algorithm for computing the operational space inertia matrix (OSIM) that reduces the Computational complexity to O(N). This algorithm, the inertia propagation method, is based on a single recursion that begins at the base of the manipulator and progresses out to the last link. Also applicable to redundant systems and mechanisms with multiple-degree-of-freedom joints, the inertia propagation method is the most efficient method known for computing Lambda for N>or=6. The numerical accuracy of the algorithm is discussed for a PUMA 560 robot with a fixed base. >
S. Bannour - One of the best experts on this subject based on the ideXlab platform.
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Principal component extraction using Recursive least squares learning
IEEE Transactions on Neural Networks, 1995Co-Authors: S. BannourAbstract:A new neural network-based approach is introduced for Recursive Computation of the principal components of a stationary vector stochastic process. The neurons of a single-layer network are sequentially trained using a Recursive least squares squares (RLS) type algorithm to extract the principal components of the input process. The optimality criterion is based on retaining the maximum information contained in the input sequence so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean squared error. The proof of the convergence of the weight vectors to the principal eigenvectors is also established. A simulation example is given to show the accuracy and speed advantages of this algorithm in comparison with the existing methods. Finally, the application of this learning algorithm to image data reduction and filtering of images degraded by additive and/or multiplicative noise is considered.
Fabien Panloup - One of the best experts on this subject based on the ideXlab platform.
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Recursive Computation of the invariant measure of a stochastic differential equation driven by a levy process
Annals of Applied Probability, 2008Co-Authors: Fabien PanloupAbstract:We investigate some Recursive procedures based on an exact or ``approximate'' Euler scheme with decreasing step in vue to Computation of invariant measures of solutions to S.D.E. driven by a Levy process. Our results are valid for a large class of S.D.E. that can be governed by Levy processes with few moments or can have a weakly mean-reverting drift, and permit to find again the a.s. C.L.T for stable processes.
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Recursive Computation of the invariant measure of a stochastic differential equation driven by a l evy process
arXiv: Probability, 2005Co-Authors: Fabien PanloupAbstract:We investigate some Recursive procedures based on an exact or ``approximate'' Euler scheme with decreasing step in vue to Computation of invariant measures of solutions to S.D.E. driven by a L\'evy process. Our results are valid for a large class of S.D.E. that can be governed by L\'evy processes with few moments or can have a weakly mean-reverting drift, and permit to find again the a.s. C.L.T for stable processes.
Lav R. Varshney - One of the best experts on this subject based on the ideXlab platform.
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Noisy In-Memory Recursive Computation with Memristor Crossbars
2020 IEEE International Symposium on Information Theory (ISIT), 2020Co-Authors: Elsa Dupraz, Lav R. VarshneyAbstract:This paper considers iterative dot-product Computation implemented on in-memory memristor crossbar substrates. To address the case where true memristor conductance values may differ from their target values, it introduces a theoretical framework that characterizes the effect of conductance value variations on the final Computation. For simple dot-products, the final Computation error can be approximated by a Gaussian distribution; the mean and variance values of the corresponding Gaussian distribution are provided. For iterative dot-product Computation, Recursive expressions are derived for the means and variances of the successive Computation outputs. Experiments verify the accuracy of the proposed analysis on both synthetic data and on images processed with memristor-based principal component analysis.
Gilles Pagès - One of the best experts on this subject based on the ideXlab platform.
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Recursive Computation of invariant distributions of Feller processes
Stochastic Processes and their Applications, 2020Co-Authors: Gilles Pagès, Clément ReyAbstract:Abstract This paper provides a general and abstract approach to compute invariant distributions for Feller processes. More precisely, we show that the Recursive algorithm presented in Lamberton and Pages (2002) and based on simulation algorithms of stochastic schemes with decreasing steps can be used to build invariant measures for general Feller processes. We also propose various applications: Approximation of Markov Brownian diffusion stationary regimes with a Milstein or an Euler scheme and approximation of a Markov switching Brownian diffusion stationary regimes using an Euler scheme.
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Recursive Computation of the invariant distributions of Feller processes: Revisited examples and new applications
Monte Carlo Methods and Applications, 2019Co-Authors: Gilles Pagès, Clément ReyAbstract:In this paper, we show that the abstract framework developed in [G. Pages and C. Rey, Recursive Computation of the invariant distribution of Markov and Feller processes, preprint 2017, https://arxiv.org/abs/1703.04557] and inspired by [D. Lamberton and G. Pages, Recursive Computation of the invariant distribution of a diffusion, Bernoulli 8 2002, 3, 367–405] can be used to build invariant distributions for Brownian diffusion processes using the Milstein scheme and for diffusion processes with censored jump using the Euler scheme. Both studies rely on a weakly mean-reverting setting for both cases. For the Milstein scheme we prove the convergence for test functions with polynomial (Wasserstein convergence) and exponential growth. For the Euler scheme of diffusion processes with censored jump we prove the convergence for test functions with polynomial growth.
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Recursive Computation of the invariant distributions of feller processes original applications
2017Co-Authors: Gilles PagèsAbstract:In this paper, we show that the abstract framework developed in \cite{Pages_Rey_2017} and inspired by \cite{Lamberton_Pages_2002} can be used to build invariant distributions for Brownian diffusion processes using the Milstein scheme and for diffusion processes with censored jump using the Euler scheme. Both studies rely on a weakly mean reverting setting for both cases. For the Milstein scheme we prove the convergence for test functions with polynomial (Wasserstein convergence) and exponential growth. For the Euler scheme of diffusion processes with censored jump we prove the convergence for test functions with polynomial growth.
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Recursive Computation of the invariant distributions of feller processes revisited examples and new applications
arXiv: Probability, 2017Co-Authors: Gilles Pagès, Clément ReyAbstract:In this paper, we show that the abstract framework developed in Pages & Rey (2017) and inspired by Lamberton & Pages (2002) can be used to build invariant distributions for Brownian diffusion processes using the Milstein scheme and for diffusion processes with censored jump using the Euler scheme. Both studies rely on a weakly mean reverting setting for both cases. For the Milstein scheme we prove the convergence for test functions with polynomial (Wasserstein convergence) and exponential growth. For the Euler scheme of diffusion processes with censored jump we prove the convergence for test functions with polynomial growth.
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Recursive Computation of the invariant distribution of markov and feller processes
arXiv: Probability, 2017Co-Authors: Gilles Pagès, Clément ReyAbstract:This paper provides a general and abstract approach to approximate ergodic regimes of Markov and Feller processes. More precisely, we show that the Recursive algorithm presented by Lamberton an Pages in 2002, and based on simulation algorithms of stochastic schemes with decreasing step can be used to build invariant measures for general Markov and Feller processes. We also propose applications in three different configurations: Approximation of Markov switching Brownian diffusion ergodic regimes using Euler scheme, approximation of Markov Brownian diffusion ergodic regimes with Milstein scheme and approximation of general diffusions with jump components ergodic regimes.