Academic literature on the topic 'Neural network adaptation'
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Journal articles on the topic "Neural network adaptation"
Hylton, Todd. "Thermodynamic Neural Network." Entropy 22, no. 3 (February 25, 2020): 256. http://dx.doi.org/10.3390/e22030256.
Full textVreeswijk, C. van, and D. Hansel. "Patterns of Synchrony in Neural Networks with Spike Adaptation." Neural Computation 13, no. 5 (May 1, 2001): 959–92. http://dx.doi.org/10.1162/08997660151134280.
Full textXie, Xurong, Xunying Liu, Tan Lee, and Lan Wang. "Bayesian Learning for Deep Neural Network Adaptation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 2096–110. http://dx.doi.org/10.1109/taslp.2021.3084072.
Full textPatre, P. M., S. Bhasin, Z. D. Wilcox, and W. E. Dixon. "Composite Adaptation for Neural Network-Based Controllers." IEEE Transactions on Automatic Control 55, no. 4 (April 2010): 944–50. http://dx.doi.org/10.1109/tac.2010.2041682.
Full textYu, D. L., and T. K. Chang. "Adaptation of diagonal recurrent neural network model." Neural Computing and Applications 14, no. 3 (March 23, 2005): 189–97. http://dx.doi.org/10.1007/s00521-004-0453-9.
Full textJoty, Shafiq, Nadir Durrani, Hassan Sajjad, and Ahmed Abdelali. "Domain adaptation using neural network joint model." Computer Speech & Language 45 (September 2017): 161–79. http://dx.doi.org/10.1016/j.csl.2016.12.006.
Full textDenker, John S. "Neural network models of learning and adaptation." Physica D: Nonlinear Phenomena 22, no. 1-3 (October 1986): 216–32. http://dx.doi.org/10.1016/0167-2789(86)90242-3.
Full textYAEGER, LARRY S. "IDENTIFYING NEURAL NETWORK TOPOLOGIES THAT FOSTER DYNAMICAL COMPLEXITY." Advances in Complex Systems 16, no. 02n03 (May 2013): 1350032. http://dx.doi.org/10.1142/s021952591350032x.
Full textZiemke, Tom. "Radar Image Segmentation Using Self-Adapting Recurrent Networks." International Journal of Neural Systems 08, no. 01 (February 1997): 47–54. http://dx.doi.org/10.1142/s0129065797000070.
Full textLi, Xiaofeng, Suying Xiang, Pengfei Zhu, and Min Wu. "Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications." International Journal of Bifurcation and Chaos 25, no. 14 (December 30, 2015): 1540030. http://dx.doi.org/10.1142/s0218127415400301.
Full textDissertations / Theses on the topic "Neural network adaptation"
Donati, Lorenzo. "Domain Adaptation through Deep Neural Networks for Health Informatics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14888/.
Full textHaskey, Stephen. "A modified One-Class-One-Network ANN architecture for dynamic phoneme adaptation." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/12099.
Full textWen, Tsung-Hsien. "Recurrent neural network language generation for dialogue systems." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275648.
Full textGangireddy, Siva Reddy. "Recurrent neural network language models for automatic speech recognition." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28990.
Full textTomashenko, Natalia. "Speaker adaptation of deep neural network acoustic models using Gaussian mixture model framework in automatic speech recognition systems." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1040/document.
Full textDifferences between training and testing conditions may significantly degrade recognition accuracy in automatic speech recognition (ASR) systems. Adaptation is an efficient way to reduce the mismatch between models and data from a particular speaker or channel. There are two dominant types of acoustic models (AMs) used in ASR: Gaussian mixture models (GMMs) and deep neural networks (DNNs). The GMM hidden Markov model (GMM-HMM) approach has been one of the most common technique in ASR systems for many decades. Speaker adaptation is very effective for these AMs and various adaptation techniques have been developed for them. On the other hand, DNN-HMM AMs have recently achieved big advances and outperformed GMM-HMM models for various ASR tasks. However, speaker adaptation is still very challenging for these AMs. Many adaptation algorithms that work well for GMMs systems cannot be easily applied to DNNs because of the different nature of these models. The main purpose of this thesis is to develop a method for efficient transfer of adaptation algorithms from the GMM framework to DNN models. A novel approach for speaker adaptation of DNN AMs is proposed and investigated. The idea of this approach is based on using so-called GMM-derived features as input to a DNN. The proposed technique provides a general framework for transferring adaptation algorithms, developed for GMMs, to DNN adaptation. It is explored for various state-of-the-art ASR systems and is shown to be effective in comparison with other speaker adaptation techniques and complementary to them
Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.
Full textPalapelas, Kantola Philip. "Extreme Quantile Estimation of Downlink Radio Channel Quality." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177657.
Full textFic, Miloslav. "Adaptace parametrů ve fuzzy systémech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221163.
Full textVu, Hien Duc. "Adaptation des méthodes d'apprentissage automatique pour la détection de défauts d'arc électriques." Electronic Thesis or Diss., Université de Lorraine, 2019. http://docnum.univ-lorraine.fr/ulprive/DDOC_T_2019_0152_VU.pdf.
Full textThe detection of electric arcs occurring in an electrical network by machine learning approaches represents the heart of the work presented in this thesis. The problem was first considered as a classification of fixed-size time series with two classes: normal and default. This first part is based on the work of the literature where the detection algorithms are organized mainly on a step of the transformation of the signals acquired on the network, followed by a step of extraction of descriptive characteristics and finally a step of decision. The multi-criteria approach adopted here aims to respond to systematic classification errors. A methodology for selecting the best combinations, transformation, and descriptors has been proposed by using learning solutions. As the development of relevant descriptors is always difficult, differents solutions offered by deep learning has also been studied. In a second phase, the study focused on the variable aspects in time of the fault detection. Two statistical decision paths have been explored, one based on the sequential probabilistic test (SPRT) and the other based on artificial neural networks LSTM (Long Short Time Memory Network). Each of these two methods exploits in its way the duration a first classification step between 0 and 1 (normal, default). The decision by SPRT uses an integration of the initial classification. LSTM learns to classify data with variable time. The results of the LSTM network are very promising, but there are a few things to explore. All of this work is based on experiments with the most complete and broadest possible data on the field of 230V alternative networks in a domestic and industrial context. The accuracy obtained is close to 100% in the majority of situations
Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.
Full textBooks on the topic "Neural network adaptation"
Lee, Tsu-Chang. Structure level adaptation for artificial neural networks. Boston: Kluwer Academic Publishers, 1991.
Find full textLee, Tsu-Chang. Structure Level Adaptation for Artificial Neural Networks. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3954-4.
Full textLee, Tsu-Chang. Structure Level Adaptation for Artificial Neural Networks. Boston, MA: Springer US, 1991.
Find full textStonier, Russel J., and Xing Huo Yu. Complex systems: Mechanism of adaptation. Amsterdam: IOS Press, 1994.
Find full textNeuronal adaptation theory: Including 29 exercises with solutions, 43 essential ideas, and 108 partially couloured figures, experiment explanations, and general theorems. Frankfurt am Main: Peter Lang, 1996.
Find full text1931-, Haykin Simon S., ed. Kalman filtering and neural networks. New York: Wiley, 2001.
Find full textJ, Stonier Russel, and Xing Huo-yu, eds. Complex systems: Mechanism of adaptation. Amsterdam: IOS Press, 1994.
Find full textFocus, Symposium on Learning and Adaptation in Stochastic and Statistical Systems (2001 Baden-Baden Germany). Proceedings of the Focus Symposium on Learning and Adaptation in Stochastic and Statistical Systems. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2002.
Find full textMarcello, Pucci, and Vitale Gianpaolo, eds. Power converters and AC electrical drives with linear neutral networks. Boca Raton: CRC Press, 2012.
Find full textChannel-Mismatch Compensation in Speaker Identification Feature Selection and Adaptation with Artificial Neural Networks. Storming Media, 1998.
Find full textBook chapters on the topic "Neural network adaptation"
Ljung, L., J. Sjöberg, and H. Hjalmarsson. "On Neural Network Model Structures in System Identification." In Identification, Adaptation, Learning, 366–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03295-4_9.
Full textCai, ManJun, JinCun Liu, GuangJun Tian, XueJian Zhang, and TiHua Wu. "Hybrid Neural Network Controller Using Adaptation Algorithm." In Advances in Neural Networks – ISNN 2007, 148–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_19.
Full textPatil, Dipali Himmatrao, and Amit Gadekar. "Tuberculosis Detection Using a Deep Neural Network." In Proceedings in Adaptation, Learning and Optimization, 600–608. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31164-2_51.
Full textHozjan, Tomaž, Goran Turk, and Iztok Fister. "Hybrid Artificial Neural Network for Fire Analysis of Steel Frames." In Adaptation, Learning, and Optimization, 149–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14400-9_7.
Full textKursin, Andrei. "Neural Network: Input Anticipation May Lead to Advanced Adaptation Properties." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 779–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_93.
Full textLee, Tsu-Chang. "Application Example: An Adaptive Neural Network Source Coder." In Structure Level Adaptation for Artificial Neural Networks, 135–53. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3954-4_5.
Full textVidyasagar, M. "An Overview of Computational Learning Theory and Its Applications to Neural Network Training." In Identification, Adaptation, Learning, 400–422. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03295-4_10.
Full textYang, Yongxin, and Timothy M. Hospedales. "Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives." In Domain Adaptation in Computer Vision Applications, 291–309. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58347-1_16.
Full textZajíc, Zbyněk, Jan Zelinka, Jan Vaněk, and Luděk Müller. "Convolutional Neural Network for Refinement of Speaker Adaptation Transformation." In Speech and Computer, 161–68. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_20.
Full textBureš, Tomáš, Petr Hnětynka, Martin Kruliš, František Plášil, Danylo Khalyeyev, Sebastian Hahner, Stephan Seifermann, Maximilian Walter, and Robert Heinrich. "Attuning Adaptation Rules via a Rule-Specific Neural Network." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, 215–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_14.
Full textConference papers on the topic "Neural network adaptation"
Li, Jinyu, Jui-Ting Huang, and Yifan Gong. "Factorized adaptation for deep neural network." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854662.
Full textJae Hoon Jeong and Soo-Young Lee. "Speaker adaptation based on judge network with small adaptation words." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.859377.
Full textSteffens Henrique, Alisson, Vinicius Almeida dos Santos, and Rodrigo Lyra. "NEAT Snake: a both evolutionary and neural network adaptation approach." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p052-053.
Full textWu, Chunwei, Guitao Cao, Wenming Cao, Hong Wang, and He Ren. "Debiased Prototype Network for Adversarial Domain Adaptation." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533346.
Full textVesely, Karel, Shinji Watanabe, Katerina Zmolikova, Martin Karafiat, Lukas Burget, and Jan Honza Cernocky. "Sequence summarizing neural network for speaker adaptation." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472692.
Full textPatre, Parag M., Shubhendu Bhasin, Zachary D. Wilcox, and Warren E. Dixon. "Composite adaptation for neural network-based controllers." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400453.
Full textMa, Min, Michael Nirschl, Fadi Biadsy, and Shankar Kumar. "Approaches for Neural-Network Language Model Adaptation." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1310.
Full textKimoto, T., Y. Yaginuma, S. Nagata, and K. Asakawa. "Inverse modeling of dynamical system-network architecture with identification network and adaptation network." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170460.
Full textSzekely, Geza, and Thomas Lindblad. "Parameter adaptation in a simplified pulse-coupled neural network." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343046.
Full textMuniz, L. F., C. N. Lintzmayer, C. Jutten, and D. G. Fantinato. "Neuroevolutive Strategies for Topology and Weights Adaptation of Artificial Neural Networks." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227807.
Full textReports on the topic "Neural network adaptation"
Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.
Full textKosko, Bart. Stability and Adaptation of Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada230108.
Full textYatsymirska, Mariya. KEY IMPRESSIONS OF 2020 IN JOURNALISTIC TEXTS. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11107.
Full textSeginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
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