Artículos de revistas sobre el tema "Reservoir computing networks"
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Van der Sande, Guy, Daniel Brunner y Miguel C. Soriano. "Advances in photonic reservoir computing". Nanophotonics 6, n.º 3 (12 de mayo de 2017): 561–76. http://dx.doi.org/10.1515/nanoph-2016-0132.
Texto completoGhosh, Sanjib, Kohei Nakajima, Tanjung Krisnanda, Keisuke Fujii y Timothy C. H. Liew. "Quantum Neuromorphic Computing with Reservoir Computing Networks". Advanced Quantum Technologies 4, n.º 9 (9 de julio de 2021): 2100053. http://dx.doi.org/10.1002/qute.202100053.
Texto completoRohm, Andre, Lina Jaurigue y Kathy Ludge. "Reservoir Computing Using Laser Networks". IEEE Journal of Selected Topics in Quantum Electronics 26, n.º 1 (enero de 2020): 1–8. http://dx.doi.org/10.1109/jstqe.2019.2927578.
Texto completoDamicelli, Fabrizio, Claus C. Hilgetag y Alexandros Goulas. "Brain connectivity meets reservoir computing". PLOS Computational Biology 18, n.º 11 (16 de noviembre de 2022): e1010639. http://dx.doi.org/10.1371/journal.pcbi.1010639.
Texto completoAntonik, Piotr, Serge Massar y Guy Van Der Sande. "Photonic reservoir computing using delay dynamical systems". Photoniques, n.º 104 (septiembre de 2020): 45–48. http://dx.doi.org/10.1051/photon/202010445.
Texto completoTran, Dat y Christof Teuscher. "Computational Capacity of Complex Memcapacitive Networks". ACM Journal on Emerging Technologies in Computing Systems 17, n.º 2 (abril de 2021): 1–25. http://dx.doi.org/10.1145/3445795.
Texto completoSenn, Christoph Walter y Itsuo Kumazawa. "Abstract Reservoir Computing". AI 3, n.º 1 (10 de marzo de 2022): 194–210. http://dx.doi.org/10.3390/ai3010012.
Texto completoHart, Joseph D., Laurent Larger, Thomas E. Murphy y Rajarshi Roy. "Delayed dynamical systems: networks, chimeras and reservoir computing". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377, n.º 2153 (22 de julio de 2019): 20180123. http://dx.doi.org/10.1098/rsta.2018.0123.
Texto completoS Pathak, Shantanu y D. Rajeswara Rao. "Reservoir Computing for Healthcare Analytics". International Journal of Engineering & Technology 7, n.º 2.32 (31 de mayo de 2018): 240. http://dx.doi.org/10.14419/ijet.v7i2.32.15576.
Texto completoAndrecut, M. "Reservoir computing on the hypersphere". International Journal of Modern Physics C 28, n.º 07 (julio de 2017): 1750095. http://dx.doi.org/10.1142/s0129183117500954.
Texto completoAkai-Kasaya, Megumi. "(Invited) Neuromorphic Devices and Systems Using Carbon Nanotubes". ECS Meeting Abstracts MA2022-01, n.º 10 (7 de julio de 2022): 778. http://dx.doi.org/10.1149/ma2022-0110778mtgabs.
Texto completoThivierge, Jean-Philippe, Eloïse Giraud, Michael Lynn y Annie Théberge Charbonneau. "Key role of neuronal diversity in structured reservoir computing". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, n.º 11 (noviembre de 2022): 113130. http://dx.doi.org/10.1063/5.0111131.
Texto completoYamaguti, Yutaka y Ichiro Tsuda. "Functional differentiations in evolutionary reservoir computing networks". Chaos: An Interdisciplinary Journal of Nonlinear Science 31, n.º 1 (enero de 2021): 013137. http://dx.doi.org/10.1063/5.0019116.
Texto completode Vos, N. J. "Reservoir computing as an alternative to traditional artificial neural networks in rainfall-runoff modelling". Hydrology and Earth System Sciences Discussions 9, n.º 5 (11 de mayo de 2012): 6101–34. http://dx.doi.org/10.5194/hessd-9-6101-2012.
Texto completoFerreira, Tiago D., Nuno A. Silva, Duarte Silva, Carla C. Rosa y Ariel Guerreiro. "Reservoir computing with nonlinear optical media". Journal of Physics: Conference Series 2407, n.º 1 (1 de diciembre de 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2407/1/012019.
Texto completoObst, Oliver, Adrian Trinchi, Simon G. Hardin, Matthew Chadwick, Ivan Cole, Tim H. Muster, Nigel Hoschke et al. "Nano-scale reservoir computing". Nano Communication Networks 4, n.º 4 (diciembre de 2013): 189–96. http://dx.doi.org/10.1016/j.nancom.2013.08.005.
Texto completoHanias, Michael P., Spyros P. Georgopoulos, Stavros G. Stavrinides, Panagiotis Tziatzios y Ioannis P. Antoniades. "Reservoir computing vs. neural networks in financial forecasting". International Journal of Computational Economics and Econometrics 1, n.º 1 (2021): 1. http://dx.doi.org/10.1504/ijcee.2021.10041721.
Texto completoGallicchio, Claudio y Alessio Micheli. "Echo State Property of Deep Reservoir Computing Networks". Cognitive Computation 9, n.º 3 (5 de mayo de 2017): 337–50. http://dx.doi.org/10.1007/s12559-017-9461-9.
Texto completoGeorgopoulos, Spyros P., Panagiotis Tziatzios, Stavros G. Stavrinides, Ioannis P. Antoniades y Michael P. Hanias. "Reservoir computing vs. neural networks in financial forecasting". International Journal of Computational Economics and Econometrics 13, n.º 1 (2023): 1. http://dx.doi.org/10.1504/ijcee.2023.127283.
Texto completoRöhm, André y Kathy Lüdge. "Multiplexed networks: reservoir computing with virtual and real nodes". Journal of Physics Communications 2, n.º 8 (3 de agosto de 2018): 085007. http://dx.doi.org/10.1088/2399-6528/aad56d.
Texto completoOZTURK, MUSTAFA C. y JOSE C. PRINCIPE. "FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES". New Mathematics and Natural Computation 05, n.º 01 (marzo de 2009): 265–86. http://dx.doi.org/10.1142/s179300570900126x.
Texto completoBüsing, Lars, Benjamin Schrauwen y Robert Legenstein. "Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons". Neural Computation 22, n.º 5 (mayo de 2010): 1272–311. http://dx.doi.org/10.1162/neco.2009.01-09-947.
Texto completoAllwood, Dan A., Matthew O. A. Ellis, David Griffin, Thomas J. Hayward, Luca Manneschi, Mohammad F. KH Musameh, Simon O'Keefe et al. "A perspective on physical reservoir computing with nanomagnetic devices". Applied Physics Letters 122, n.º 4 (23 de enero de 2023): 040501. http://dx.doi.org/10.1063/5.0119040.
Texto completoBasterrech, Sebastián y Gerardo Rubino. "ECHO STATE QUEUEING NETWORKS: A COMBINATION OF RESERVOIR COMPUTING AND RANDOM NEURAL NETWORKS". Probability in the Engineering and Informational Sciences 31, n.º 4 (17 de mayo de 2017): 457–76. http://dx.doi.org/10.1017/s0269964817000110.
Texto completoRen, Bin y Huanfei Ma. "Global optimization of hyper-parameters in reservoir computing". Electronic Research Archive 30, n.º 7 (2022): 2719–29. http://dx.doi.org/10.3934/era.2022139.
Texto completoGonon, Lukas y Juan-Pablo Ortega. "Reservoir Computing Universality With Stochastic Inputs". IEEE Transactions on Neural Networks and Learning Systems 31, n.º 1 (enero de 2020): 100–112. http://dx.doi.org/10.1109/tnnls.2019.2899649.
Texto completoZhong, Xijuan y Shuai Wang. "Learning Coupled Oscillators System with Reservoir Computing". Symmetry 14, n.º 6 (25 de mayo de 2022): 1084. http://dx.doi.org/10.3390/sym14061084.
Texto completoCarbajal, Juan Pablo, Joni Dambre, Michiel Hermans y Benjamin Schrauwen. "Memristor Models for Machine Learning". Neural Computation 27, n.º 3 (marzo de 2015): 725–47. http://dx.doi.org/10.1162/neco_a_00694.
Texto completoAndreev, Andrey V., Artem A. Badarin, Vladimir A. Maximenko y Alexander E. Hramov. "Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, n.º 10 (octubre de 2022): 103126. http://dx.doi.org/10.1063/5.0114127.
Texto completoHermans, Michiel y Benjamin Schrauwen. "Recurrent Kernel Machines: Computing with Infinite Echo State Networks". Neural Computation 24, n.º 1 (enero de 2012): 104–33. http://dx.doi.org/10.1162/neco_a_00200.
Texto completoYilmaz, Ozgur. "Symbolic Computation Using Cellular Automata-Based Hyperdimensional Computing". Neural Computation 27, n.º 12 (diciembre de 2015): 2661–92. http://dx.doi.org/10.1162/neco_a_00787.
Texto completoSteiner, Peter, Azarakhsh Jalalvand, Simon Stone y Peter Birkholz. "PyRCN: A toolbox for exploration and application of Reservoir Computing Networks". Engineering Applications of Artificial Intelligence 113 (agosto de 2022): 104964. http://dx.doi.org/10.1016/j.engappai.2022.104964.
Texto completoHamedani, Kian, Lingjia Liu, Rachad Atat, Jinsong Wu y Yang Yi. "Reservoir Computing Meets Smart Grids: Attack Detection Using Delayed Feedback Networks". IEEE Transactions on Industrial Informatics 14, n.º 2 (febrero de 2018): 734–43. http://dx.doi.org/10.1109/tii.2017.2769106.
Texto completoJalalvand, Azarakhsh, Kris Demuynck, Wesley De Neve y Jean-Pierre Martens. "On the application of reservoir computing networks for noisy image recognition". Neurocomputing 277 (febrero de 2018): 237–48. http://dx.doi.org/10.1016/j.neucom.2016.11.100.
Texto completoMastoi, Qurat-ul-ain, Teh Wah y Ram Gopal Raj. "Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification". Applied Sciences 9, n.º 4 (18 de febrero de 2019): 702. http://dx.doi.org/10.3390/app9040702.
Texto completoLe, Phuong Y., Billy J. Murdoch, Anders J. Barlow, Anthony S. Holland, Dougal G. McCulloch, Chris F. McConville y Jim G. Partridge. "Electroformed, Self‐Connected Tin Oxide Nanoparticle Networks for Electronic Reservoir Computing". Advanced Electronic Materials 6, n.º 7 (20 de mayo de 2020): 2000081. http://dx.doi.org/10.1002/aelm.202000081.
Texto completoHosseini, Mahshid, Nikolay Frick, Damien Guilbaud, Ming Gao y Thomas H. LaBean. "Resistive switching of two-dimensional Ag2S nanowire networks for neuromorphic applications". Journal of Vacuum Science & Technology B 40, n.º 4 (julio de 2022): 043201. http://dx.doi.org/10.1116/6.0001867.
Texto completoMujal, Pere, Johannes Nokkala, Rodrigo Martínez-Peña, Gian Luca Giorgi, Miguel C. Soriano y Roberta Zambrini. "Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing". Journal of Physics: Complexity 2, n.º 4 (15 de noviembre de 2021): 045008. http://dx.doi.org/10.1088/2632-072x/ac340e.
Texto completoShirai, Shota, Susant Kumar Acharya, Saurabh Kumar Bose, Joshua Brian Mallinson, Edoardo Galli, Matthew D. Pike, Matthew D. Arnold y Simon Anthony Brown. "Long-range temporal correlations in scale-free neuromorphic networks". Network Neuroscience 4, n.º 2 (enero de 2020): 432–47. http://dx.doi.org/10.1162/netn_a_00128.
Texto completoZhou, Zhou, Lingjia Liu, Vikram Chandrasekhar, Jianzhong Zhang y Yang Yi. "Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 1266–73. http://dx.doi.org/10.1609/aaai.v34i01.5481.
Texto completoElbedwehy, Aya N., Awny M. El-Mohandes, Ahmed Elnakib y Mohy Eldin Abou-Elsoud. "FPGA-based reservoir computing system for ECG denoising". Microprocessors and Microsystems 91 (junio de 2022): 104549. http://dx.doi.org/10.1016/j.micpro.2022.104549.
Texto completoCucchi, Matteo, Christopher Gruener, Lautaro Petrauskas, Peter Steiner, Hsin Tseng, Axel Fischer, Bogdan Penkovsky et al. "Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification". Science Advances 7, n.º 34 (agosto de 2021): eabh0693. http://dx.doi.org/10.1126/sciadv.abh0693.
Texto completoAthanasiou, Vasileios y Zoran Konkoli. "On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks". International Journal of Parallel, Emergent and Distributed Systems 33, n.º 4 (25 de febrero de 2017): 367–86. http://dx.doi.org/10.1080/17445760.2017.1287264.
Texto completoViero, Yannick, David Guérin, Anton Vladyka, Fabien Alibart, Stéphane Lenfant, M. Calame y Dominique Vuillaume. "Light-Stimulatable Molecules/Nanoparticles Networks for Switchable Logical Functions and Reservoir Computing". Advanced Functional Materials 28, n.º 39 (6 de agosto de 2018): 1801506. http://dx.doi.org/10.1002/adfm.201801506.
Texto completoAlomar, Miquel L., Vincent Canals, Nicolas Perez-Mora, Víctor Martínez-Moll y Josep L. Rosselló. "FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting". Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3917892.
Texto completoFrady, E. Paxon, Denis Kleyko y Friedrich T. Sommer. "A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks". Neural Computation 30, n.º 6 (junio de 2018): 1449–513. http://dx.doi.org/10.1162/neco_a_01084.
Texto completoJohnson, Chris, Andrew Philippides y Philip Husbands. "Active Shape Discrimination with Compliant Bodies as Reservoir Computers". Artificial Life 22, n.º 2 (mayo de 2016): 241–68. http://dx.doi.org/10.1162/artl_a_00202.
Texto completoDion, Yves y Saad Bennis. "A global modeling approach to the hydraulic performance evaluation of a sewer network". Canadian Journal of Civil Engineering 37, n.º 11 (noviembre de 2010): 1432–36. http://dx.doi.org/10.1139/l10-082.
Texto completoDi, Sarli, Claudio Gallicchio y Alessio Micheli. "On the effectiveness of Gated Echo State Networks for data exhibiting long-term dependencies". Computer Science and Information Systems 19, n.º 1 (2022): 379–96. http://dx.doi.org/10.2298/csis210218063d.
Texto completoSillin, Henry O., Renato Aguilera, Hsien-Hang Shieh, Audrius V. Avizienis, Masakazu Aono, Adam Z. Stieg y James K. Gimzewski. "A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing". Nanotechnology 24, n.º 38 (2 de septiembre de 2013): 384004. http://dx.doi.org/10.1088/0957-4484/24/38/384004.
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