Littérature scientifique sur le sujet « Matrix pseudoinversion »
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Articles de revues sur le sujet "Matrix pseudoinversion"
Kornilova, Mariya, Vladislav Kovalnogov, Ruslan Fedorov, Mansur Zamaleev, Vasilios N. Katsikis, Spyridon D. Mourtas et Theodore E. Simos. « Zeroing Neural Network for Pseudoinversion of an Arbitrary Time-Varying Matrix Based on Singular Value Decomposition ». Mathematics 10, no 8 (7 avril 2022) : 1208. http://dx.doi.org/10.3390/math10081208.
Texte intégralKononov, M. V., O. A. Nagulyak, A. V. Netreba et A. A. Sudakov. « Reconstruction in NMR by the method of signal matrix pseudoinversion ». Radioelectronics and Communications Systems 51, no 10 (octobre 2008) : 531–33. http://dx.doi.org/10.3103/s0735272708100038.
Texte intégralXiang, Qiuhong, Bolin Liao, Lin Xiao, Long Lin et Shuai Li. « Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion ». Soft Computing 23, no 3 (8 mars 2018) : 755–66. http://dx.doi.org/10.1007/s00500-018-3119-8.
Texte intégralStanimirović, Predrag S., Spyridon D. Mourtas, Vasilios N. Katsikis, Lev A. Kazakovtsev et Vladimir N. Krutikov. « Recurrent Neural Network Models Based on Optimization Methods ». Mathematics 10, no 22 (16 novembre 2022) : 4292. http://dx.doi.org/10.3390/math10224292.
Texte intégralLiao, Bolin, et Qiuhong Xiang. « Robustness Analyses and Optimal Sampling Gap of Recurrent Neural Network for Dynamic Matrix Pseudoinversion ». Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no 5 (20 septembre 2017) : 778–84. http://dx.doi.org/10.20965/jaciii.2017.p0778.
Texte intégralAlharbi, Hadeel, Houssem Jerbi, Mourad Kchaou, Rabeh Abbassi, Theodore E. Simos, Spyridon D. Mourtas et Vasilios N. Katsikis. « Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks ». Mathematics 11, no 3 (24 janvier 2023) : 600. http://dx.doi.org/10.3390/math11030600.
Texte intégralHu, Zeshan, Lin Xiao, Kenli Li, Keqin Li et Jichun Li. « Performance analysis of nonlinear activated zeroing neural networks for time-varying matrix pseudoinversion with application ». Applied Soft Computing 98 (janvier 2021) : 106735. http://dx.doi.org/10.1016/j.asoc.2020.106735.
Texte intégralKohno, Kiyotaka, Mitsuru Kawamoto et Yujiro Inouye. « A Matrix Pseudoinversion Lemma and Its Application to Block-Based Adaptive Blind Deconvolution for MIMO Systems ». IEEE Transactions on Circuits and Systems I : Regular Papers 57, no 7 (juillet 2010) : 1449–62. http://dx.doi.org/10.1109/tcsi.2010.2050222.
Texte intégralJin, Long, Shuai Li, Huanqing Wang et Zhijun Zhang. « Nonconvex projection activated zeroing neurodynamic models for time-varying matrix pseudoinversion with accelerated finite-time convergence ». Applied Soft Computing 62 (janvier 2018) : 840–50. http://dx.doi.org/10.1016/j.asoc.2017.09.016.
Texte intégralSimos, Theodore E., Vasilios N. Katsikis, Spyridon D. Mourtas, Predrag S. Stanimirović et Dimitris Gerontitis. « A higher-order zeroing neural network for pseudoinversion of an arbitrary time-varying matrix with applications to mobile object localization ». Information Sciences 600 (juillet 2022) : 226–38. http://dx.doi.org/10.1016/j.ins.2022.03.094.
Texte intégralThèses sur le sujet "Matrix pseudoinversion"
GALVAN, Stefano. « Perception-motivated parallel algorithms for haptics ». Doctoral thesis, Università degli Studi di Verona, 2010. http://hdl.handle.net/11562/343948.
Texte intégralIn the last years the use of haptic feedback has been used in several applications, from mobile phones to rehabilitation, from video games to robotic aided surgery. The haptic devices, that are the interfaces that create the stimulation and reproduce the physical interaction with virtual or remote environments, have been studied, analyzed and developed in many ways. Every innovation in the mechanics, electronics and technical design of the device it is valuable, however it is important to maintain the focus of the haptic interaction on the human being, who is the only user of force feedback. In this thesis we worked on two main topics that are relevant to this aim: a perception based force signal manipulation and the use of modern multicore architectures for the implementation of the haptic controller. With the help of a specific experimental setup and using a 6 dof haptic device we designed a psychophysical experiment aimed at identifying of the force/torque differential thresholds applied to the hand-arm system. On the basis of the results obtained we determined a set of task dependent scaling functions, one for each degree of freedom of the three-dimensional space, that can be used to enhance the human abilities in discriminating different stimuli. The perception based manipulation of the force feedback requires a fast, stable and configurable controller of the haptic interface. Thus a solution is to use new available multicore architectures for the implementation of the controller, but many consolidated algorithms have to be ported to these parallel systems. Focusing on specific problem, i.e. the matrix pseudoinversion, that is part of the robotics dynamic and kinematic computation, we showed that it is possible to migrate code that was already implemented in hardware, and in particular old algorithms that were inherently parallel and thus not competitive on sequential processors. The main question that still lies open is how much effort is required in order to write these algorithms, usually described in VLSI or schematics, in a modern programming language. We show that a careful task decomposition and design permit a mapping of the code on the available cores. In addition, the use of data parallelism on SIMD machines can give good performance when simple vector instructions such as add and shift operations are used. Since these instructions are present also in hardware implementations the migration can be easily performed. We tested our approach on a Sony PlayStation 3 game console equipped with IBM Cell Broadband Engine processor.
Chapitres de livres sur le sujet "Matrix pseudoinversion"
Cancelliere, Rossella, Mario Gai, Thierry Artières et Patrick Gallinari. « Matrix Pseudoinversion for Image Neural Processing ». Dans Neural Information Processing, 116–25. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_15.
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