Littérature scientifique sur le sujet « Artificial Neural Networks and Recurrent Neutral Networks »
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Articles de revues sur le sujet "Artificial Neural Networks and Recurrent Neutral Networks"
Prathibha, Dr G., Y. Kavya, P. Vinay Jacob et L. Poojita. « Speech Emotion Recognition Using Deep Learning ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no 07 (4 juillet 2024) : 1–13. http://dx.doi.org/10.55041/ijsrem36262.
Texte intégralAli, Ayesha, Ateeq Ur Rehman, Ahmad Almogren, Elsayed Tag Eldin et Muhammad Kaleem. « Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement ». Energies 15, no 20 (13 octobre 2022) : 7553. http://dx.doi.org/10.3390/en15207553.
Texte intégralPranav Kumar Chaudhary, Aakash Kishore Chotrani, Raja Mohan, Mythili Boopathi, Piyush Ranjan, Madhavi Najana,. « Ai in Fraud Detection : Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct ». Journal of Electrical Systems 20, no 3s (4 avril 2024) : 1332–38. http://dx.doi.org/10.52783/jes.1508.
Texte intégralNassif, Ali Bou, Ismail Shahin, Mohammed Lataifeh, Ashraf Elnagar et Nawel Nemmour. « Empirical Comparison between Deep and Classical Classifiers for Speaker Verification in Emotional Talking Environments ». Information 13, no 10 (27 septembre 2022) : 456. http://dx.doi.org/10.3390/info13100456.
Texte intégralLee, Hong Jae, et Tae Seog Kim. « Comparison and Analysis of SNN and RNN Results for Option Pricing and Deep Hedging Using Artificial Neural Networks (ANN) ». Academic Society of Global Business Administration 20, no 5 (30 octobre 2023) : 146–78. http://dx.doi.org/10.38115/asgba.2023.20.5.146.
Texte intégralSutskever, Ilya, et Geoffrey Hinton. « Temporal-Kernel Recurrent Neural Networks ». Neural Networks 23, no 2 (mars 2010) : 239–43. http://dx.doi.org/10.1016/j.neunet.2009.10.009.
Texte intégralWang, Rui. « Generalisation of Feed-Forward Neural Networks and Recurrent Neural Networks ». Applied and Computational Engineering 40, no 1 (21 février 2024) : 242–46. http://dx.doi.org/10.54254/2755-2721/40/20230659.
Texte intégralPoudel, Sushan, et Dr R. Anuradha. « Speech Command Recognition using Artificial Neural Networks ». JOIV : International Journal on Informatics Visualization 4, no 2 (26 mai 2020) : 73. http://dx.doi.org/10.30630/joiv.4.2.358.
Texte intégralTurner, Andrew James, et Julian Francis Miller. « Recurrent Cartesian Genetic Programming of Artificial Neural Networks ». Genetic Programming and Evolvable Machines 18, no 2 (8 août 2016) : 185–212. http://dx.doi.org/10.1007/s10710-016-9276-6.
Texte intégralZiemke, Tom. « Radar image segmentation using recurrent artificial neural networks ». Pattern Recognition Letters 17, no 4 (avril 1996) : 319–34. http://dx.doi.org/10.1016/0167-8655(95)00128-x.
Texte intégralThèses sur le sujet "Artificial Neural Networks and Recurrent Neutral Networks"
Kolen, John F. « Exploring the computational capabilities of recurrent neural networks / ». The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487853913100192.
Texte intégralShao, Yuanlong. « Learning Sparse Recurrent Neural Networks in Language Modeling ». The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398942373.
Texte intégralGudjonsson, Ludvik. « Comparison of two methods for evolving recurrent artificial neural networks for ». Thesis, University of Skövde, University of Skövde, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-155.
Texte intégraln this dissertation a comparison of two evolutionary methods for evolving ANNs for robot control is made. The methods compared are SANE with enforced sub-population and delta-coding, and marker-based encoding. In an attempt to speed up evolution, marker-based encoding is extended with delta-coding. The task selected for comparison is the hunter-prey task. This task requires the robot controller to posess some form of memory as the prey can move out of sensor range. Incremental evolution is used to evolve the complex behaviour that is required to successfully handle this task. The comparison is based on computational power needed for evolution, and complexity, robustness, and generalisation of the resulting ANNs. The results show that marker-based encoding is the most efficient method tested and does not need delta-coding to increase the speed of evolution process. Additionally the results indicate that delta-coding does not increase the speed of evolution with marker-based encoding.
Parfitt, Shan Helen. « Explorations in anaphora resolution in artificial neural networks : implications for nativism ». Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267247.
Texte intégralNAPOLI, CHRISTIAN. « A-I : Artificial intelligence ». Doctoral thesis, Università degli studi di Catania, 2016. http://hdl.handle.net/20.500.11769/490996.
Texte intégralKramer, Gregory Robert. « An analysis of neutral drift's effect on the evolution of a CTRNN locomotion controller with noisy fitness evaluation ». Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1182196651.
Texte intégralRallabandi, Pavan Kumar. « Processing hidden Markov models using recurrent neural networks for biological applications ». Thesis, University of the Western Cape, 2013. http://hdl.handle.net/11394/4525.
Texte intégralIn this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have been developed by other researchers. However, to the best of our knowledge, no algorithm for processing HMMs through learning has been given. Taking advantage of the structural similarities of the architectural dynamics of the RNNs and HMMs, in this work we analyze the combination of these two systems into the hybrid architecture. To this end, the main objective of this study is to improve the sequence recognition/classi_cation performance by applying a hybrid neural/symbolic approach. In particular, trained HMMs are used as the initial symbolic domain theory and directly encoded into appropriate RNN architecture, meaning that the prior knowledge is processed through the training of RNNs. Proposed algorithm is then implemented on sample test beds and other real time biological applications.
Salihoglu, Utku. « Toward a brain-like memory with recurrent neural networks ». Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210221.
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Based on these assumptions, this thesis provides a computer model of neural network simulation of a brain-like memory. It first shows experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the background, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause, but the consequence of the learning. However, it appears as an helpful consequence that widens the network’s encoding capacity. To learn the information to be stored, two supervised iterative Hebbian learning algorithm are proposed. One leaves the semantics of the attractors to be associated with the feeding data unprescribed, while the other defines it a priori. Both algorithms show good results, even though the first one is more robust and has a greater storing capacity. Using these promising results, a biologically plausible alternative to these algorithms is proposed using cell assemblies as substrate for information. Even though this is not new, the mechanisms underlying their formation are poorly understood and, so far, there are no biologically plausible algorithms that can explain how external stimuli can be online stored in cell assemblies. This thesis provide such a solution combining a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilizes the cell assemblies by learning the feed forward input connections. This last mechanism is inspired by the retroaxonal hypothesis.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Yang, Jidong. « Road crack condition performance modeling using recurrent Markov chains and artificial neural networks ». [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000567.
Texte intégralWillmott, Devin. « Recurrent Neural Networks and Their Applications to RNA Secondary Structure Inference ». UKnowledge, 2018. https://uknowledge.uky.edu/math_etds/58.
Texte intégralLivres sur le sujet "Artificial Neural Networks and Recurrent Neutral Networks"
Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.
Trouver le texte intégralJain, Lakhmi C., et Larry Medsker. Recurrent Neural Networks : Design and Applications. Taylor & Francis Group, 1999.
Trouver le texte intégralJain, Lakhmi C., et Larry Medsker. Recurrent Neural Networks : Design and Applications. Taylor & Francis Group, 1999.
Trouver le texte intégralGraves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012.
Trouver le texte intégralIntegration Of Swarm Intelligence And Artificial Neutral Network. World Scientific Publishing Company, 2011.
Trouver le texte intégralSangeetha, V., et S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.
Texte intégralZhang, Huaguang, Derong Liu, Zeng-Guang Hou, Changyin Sun et Shumin Fei. Advances in Neural Networks - ISNN 2007 : 4th International Symposium on Neutral Networks, ISNN 2007 Nanjing, China, June 3-7, 2007. Proceedings, Part II. Springer London, Limited, 2007.
Trouver le texte intégralZhang, Huaguang, Derong Liu, Zeng-Guang Hou, Changyin Sun et Shumin Fei. Advances in Neural Networks - ISNN 2007 : 4th International Symposium on Neutral Networks, ISNN 2007 Nanjing, China, June 3-7, 2007. Proceedings, Part I. Springer London, Limited, 2007.
Trouver le texte intégral(Editor), Derong Liu, Shumin Fei (Editor), Zeng-Guang Hou (Editor), Huaguang Zhang (Editor) et Changyin Sun (Editor), dir. Advances in Neural Networks - ISNN 2007 : 4th International Symposium on Neutral Networks, ISNN 2007Nanjing, China, June 3-7, 2007. Proceedings, Part I (Lecture Notes in Computer Science). Springer, 2007.
Trouver le texte intégralChurchland, Paul M. The Engine of Reason, the Seat of the Soul. The MIT Press, 1995. http://dx.doi.org/10.7551/mitpress/2758.001.0001.
Texte intégralChapitres de livres sur le sujet "Artificial Neural Networks and Recurrent Neutral Networks"
da Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni et Silas Franco dos Reis Alves. « Recurrent Hopfield Networks ». Dans Artificial Neural Networks, 139–55. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_7.
Texte intégralKrauss, Patrick. « Recurrent Neural Networks ». Dans Artificial Intelligence and Brain Research, 131–37. Berlin, Heidelberg : Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68980-6_14.
Texte intégralLynch, Stephen. « Recurrent Neural Networks ». Dans Python for Scientific Computing and Artificial Intelligence, 267–84. Boca Raton : Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003285816-19.
Texte intégralSharma, Arpana, Kanupriya Goswami, Vinita Jindal et Richa Gupta. « A Road Map to Artificial Neural Network ». Dans Recurrent Neural Networks, 3–21. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822-2.
Texte intégralKathirvel, A., Debashreet Das, Stewart Kirubakaran, M. Subramaniam et S. Naveneethan. « Artificial Intelligence–Based Mobile Bill Payment System Using Biometric Fingerprint ». Dans Recurrent Neural Networks, 233–45. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822-16.
Texte intégralda Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni et Silas Franco dos Reis Alves. « Forecast of Stock Market Trends Using Recurrent Networks ». Dans Artificial Neural Networks, 221–27. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_13.
Texte intégralLindgren, Kristian, Anders Nilsson, Mats G. Nordahl et Ingrid Råde. « Evolving Recurrent Neural Networks ». Dans Artificial Neural Nets and Genetic Algorithms, 55–62. Vienna : Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_9.
Texte intégralSchäfer, Anton Maximilian, et Hans Georg Zimmermann. « Recurrent Neural Networks Are Universal Approximators ». Dans Artificial Neural Networks – ICANN 2006, 632–40. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_66.
Texte intégralRiaza, Ricardo, et Pedro J. Zufiria. « Time-Scaling in Recurrent Neural Learning ». Dans Artificial Neural Networks — ICANN 2002, 1371–76. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_221.
Texte intégralHammer, Barbara. « On the Generalization Ability of Recurrent Networks ». Dans Artificial Neural Networks — ICANN 2001, 731–36. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_102.
Texte intégralActes de conférences sur le sujet "Artificial Neural Networks and Recurrent Neutral Networks"
Cao, Zhu, Linlin Wang et Gerard de Melo. « Multiple-Weight Recurrent Neural Networks ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/205.
Texte intégralWu, Hao, Ziyang Chen, Weiwei Sun, Baihua Zheng et Wei Wang. « Modeling Trajectories with Recurrent Neural Networks ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/430.
Texte intégralOmar, Tarek A., Nabih E. Bedewi et Azim Eskandarian. « Recurrent Artificial Neural Networks for Crashworthiness Analysis ». Dans ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1190.
Texte intégralMak, M. W. « Speaker identification using modular recurrent neural networks ». Dans 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950519.
Texte intégralChen, Yuexing, et Jiarun Li. « Recurrent Neural Networks algorithms and applications ». Dans 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2021. http://dx.doi.org/10.1109/icbase53849.2021.00015.
Texte intégralSharma, Shambhavi. « Emotion Recognition from Speech using Artificial Neural Networks and Recurrent Neural Networks ». Dans 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377192.
Texte intégralLee, Jinhyuk, Hyunjae Kim, Miyoung Ko, Donghee Choi, Jaehoon Choi et Jaewoo Kang. « Name Nationality Classification with Recurrent Neural Networks ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/289.
Texte intégralArgun, Aykut, Tobias Thalheim, Frank Cichos et Giovanni Volpe. « Calibration of force fields using recurrent neural networks ». Dans Emerging Topics in Artificial Intelligence 2020, sous la direction de Giovanni Volpe, Joana B. Pereira, Daniel Brunner et Aydogan Ozcan. SPIE, 2020. http://dx.doi.org/10.1117/12.2567931.
Texte intégral« INTERACTIVE EVOLVING RECURRENT NEURAL NETWORKS ARE SUPER-TURING ». Dans International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003740603280333.
Texte intégralSwanston, D. J. « Relative order defines a topology for recurrent networks ». Dans 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950564.
Texte intégralRapports d'organisations sur le sujet "Artificial Neural Networks and Recurrent Neutral Networks"
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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