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Artykuły w czasopismach na temat "Back propagation"
Chahar, Vikas. "Analysis of Back Propagation Algorithm". International Journal of Scientific Research 2, nr 8 (1.06.2012): 305–6. http://dx.doi.org/10.15373/22778179/aug2013/98.
Pełny tekst źródłaA, Dr Deepa, i Fathima Thasliya P A. "Back Propagation". International Journal for Research in Applied Science and Engineering Technology 11, nr 4 (30.04.2023): 334–39. http://dx.doi.org/10.22214/ijraset.2023.50077.
Pełny tekst źródłaHui, Hui, Dayou Liu i Yafei Wang. "Sequential back-propagation". Journal of Computer Science and Technology 9, nr 3 (lipiec 1994): 252–60. http://dx.doi.org/10.1007/bf02939506.
Pełny tekst źródłaShing Wang, Chau. "Power Disturbance Recognition Using Back-Propagation Neural Networks". International Journal of Engineering and Technology 4, nr 4 (2012): 430–33. http://dx.doi.org/10.7763/ijet.2012.v4.403.
Pełny tekst źródłaBernard, C., i D. Johnston. "Distance-Dependent Modifiable Threshold for Action Potential Back-Propagation in Hippocampal Dendrites". Journal of Neurophysiology 90, nr 3 (wrzesień 2003): 1807–16. http://dx.doi.org/10.1152/jn.00286.2003.
Pełny tekst źródłaBuscema, Massimo. "Back Propagation Neural Networks". Substance Use & Misuse 33, nr 2 (styczeń 1998): 233–70. http://dx.doi.org/10.3109/10826089809115863.
Pełny tekst źródłaSolanki, Shital. "A review on back propagation algorithms for Feedforward Networks". Global Journal For Research Analysis 2, nr 1 (15.06.2012): 73–75. http://dx.doi.org/10.15373/22778160/january2013/61.
Pełny tekst źródłaGarkani-Nejad, Zahra, i Behzad Ahmadi-Roudi. "Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives". Canadian Journal of Chemistry 91, nr 4 (kwiecień 2013): 255–62. http://dx.doi.org/10.1139/cjc-2012-0372.
Pełny tekst źródłaAL-Assady, Nidhal, Baydaa Khaleel i Shahbaa Khaleel. "Improvement the Back-propagation Technique". AL-Rafidain Journal of Computer Sciences and Mathematics 1, nr 2 (1.12.2004): 127–51. http://dx.doi.org/10.33899/csmj.2004.164115.
Pełny tekst źródłaMoody, John, i Chris Darken. "Speedy alternatives to back propagation". Neural Networks 1 (styczeń 1988): 202. http://dx.doi.org/10.1016/0893-6080(88)90239-0.
Pełny tekst źródłaRozprawy doktorskie na temat "Back propagation"
Lowton, Andrew D. "A constructive learning algorithm based on back-propagation". Thesis, Aston University, 1995. http://publications.aston.ac.uk/10663/.
Pełny tekst źródłaSanner, Robert M. (Robert Michael). "Neuromorphic regulation of dynamic systems using back propagation networks". Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/34995.
Pełny tekst źródłaFernando, Thudugala Mudalige K. G. "Hydrological applications of MLP neural networks with back-propagation". Thesis, Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25085517.
Pełny tekst źródłaBennett, Richard Campbell. "Classification of underwater signals using a back-propagation neural network". Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA331774.
Pełny tekst źródłaThesis advisors, Monique P. Fargues, Roberto Cristi. Includes bibliographical references (p. 95). Also available online.
Ramachandran, Adithya. "HEV fuel optimization using interval back propagation based dynamic programming". Thesis, Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55054.
Pełny tekst źródłaTeo, Chin Hock. "Back-propagation neural networks in adaptive control of unknown nonlinear systems". Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26898.
Pełny tekst źródłaThe objective of this research is to develop a Back-propagation Neural Network (BNN) to control certain classes of unknown nonlinear systems and explore the network's capabilities. The structure of the Direct Model Reference Adaptive Controller (DMRAC) for Linear Time Invariant (LTI) systems with unknown parameters is first analyzed. This structure is then extended using a BNN for adaptive control of unknown nonlinear systems. The specific structure of the BNN DMRAC is developed for control of four general classes of nonlinear systems modeled in discrete time. Experiments are conducted by placing a representative system from each class under the BNN's control. The condition under which the BNN DMRAC can successfully control these systems are investigated. The design and training of the BNN are also studied. The results of the experiments show that the BNN DMRAC works for the representative systems considered, while the conventional least-squares estimator DMRAC fails. Based on analysis and experimental findings, some genera conditions required to ensure that this technique works are postulated and discussed. General guidelines used to achieve the stability of the BNN learning process and good learning convergence are also discussed. To establish this as a general and significant control technique, further research is required to obtain analytically, the conditions for stability of the controlled system, and to develop more specific rules and guidelines in the BNN design and training.
Cakarcan, Alpay. "Back-propagation neural networks in adaptive control of unknown nonlinear systems". Thesis, Monterey, California. Naval Postgraduate School, 1994. http://hdl.handle.net/10945/30830.
Pełny tekst źródłaXiao, Nancy Y. (Nancy Ying). "Using the modified back-propagation algorithm to perform automated downlink analysis". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/40206.
Pełny tekst źródłaIncludes bibliographical references (p. 121-122).
by Nancy Y. Xiao.
M.Eng.
Le, Chau Giang. "Application of a back-propagation neural network to isolated-word speech recognition". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA272495.
Pełny tekst źródłaChen, Peng. "Analysis of contribution rates and prediction based on back propagation neural networks". Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691340.
Pełny tekst źródłaKsiążki na temat "Back propagation"
Yves, Chauvin, i Rumelhart David E, red. Back propagation: Theory, architectures and applications. Hillsdale: Erlbaum, 1995.
Znajdź pełny tekst źródłaLowton, Andrew David. A constructive learning algorithm based on back-propagation. Birmingham: Aston University. Department ofComputer Science and Applied Mathematics, 1995.
Znajdź pełny tekst źródłaNarendra, Kumpati S. Back propagation in dynamical systems containing neural networks. New Haven,Co: Yale University Center for Systems Science, 1989.
Znajdź pełny tekst źródłaBennett, Richard Campbell. Classification of underwater signals using a back-propagation neural network. Monterey, Calif: Naval Postgraduate School, 1997.
Znajdź pełny tekst źródłaAnil, Phatak, Chatterji Gano i Ames Research Center, red. Scene segmentation of natural images using texture measures and back-propagation. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1993.
Znajdź pełny tekst źródłaTeo, Chin Hock. Back-propagation neural networks in adaptive control of unknown nonlinear systems. Monterey, Calif: Naval Postgraduate School, 1991.
Znajdź pełny tekst źródłaWalker, James L. Back propagation neural networks for predicting ultimate strengths of unidirectional graphite/epoxy tensile specimens. [Washington, DC: National Aeronautics and Space Administration, 1993.
Znajdź pełny tekst źródłaS, Piascik Robert, i Langley Research Center, red. A back face strain compliance expression for the compact tension specimen. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.
Znajdź pełny tekst źródłaN, Sundararajan, i Foo Shou King, red. Parallel implementations of backpropagation neural networks on transputers: A study of training set parallelism. Singapore: World Scientific, 1996.
Znajdź pełny tekst źródłaPlants Plus P/back. Collins, 1987.
Znajdź pełny tekst źródłaCzęści książek na temat "Back propagation"
Trejo, Luis A., i Carlos Sandoval. "Improving back-propagation: Epsilon-back-propagation". W Lecture Notes in Computer Science, 427–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59497-3_205.
Pełny tekst źródłaHenseler, J. "Back Propagation". W Lecture Notes in Computer Science, 37–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0027022.
Pełny tekst źródłaUtgoff, Paul E. "Constraint Back-Propagation". W The Kluwer International Series in Engineering and Computer Science, 63–90. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2283-2_6.
Pełny tekst źródłaMcKeon, Richard. "Training with Back Propagation". W Neural Networks for Electronics Hobbyists, 75–98. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3507-2_5.
Pełny tekst źródłaSheu, Bing J., i Joongho Choi. "Back-Propagation Neural Networks". W Neural Information Processing and VLSI, 277–96. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2247-8_10.
Pełny tekst źródłaGasparini, Sonia, i Michele Migliore. "Action Potential Back-Propagation". W Encyclopedia of Computational Neuroscience, 1–6. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7320-6_123-5.
Pełny tekst źródłaMashinchi, M. Hadi, i Siti Mariyam H. J. Shamsuddin. "Three-Term Fuzzy Back-Propagation". W Studies in Computational Intelligence, 143–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01082-8_6.
Pełny tekst źródłaMüller, Berndt, Joachim Reinhardt i Michael T. Strickland. "BTT: Back-Propagation Through Time". W Neural Networks, 296–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_28.
Pełny tekst źródłaAlankar, Bhavya, Nowsheena Yousf i Shafqat Ul Ahsaan. "Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks". W Advances in Intelligent Systems and Computing, 99–115. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9330-3_10.
Pełny tekst źródłaBedzak, Miroslaw. "Fuzzy-η for Back Propagation Networks". W Computational Intelligence. Theory and Applications, 86–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_13.
Pełny tekst źródłaStreszczenia konferencji na temat "Back propagation"
Fazayeli, Farideh, Lipo Wang i Wen Liu. "Back-propagation with chaos". W 2008 International Conference on Neural Networks and Signal Processing (ICNNSP). IEEE, 2008. http://dx.doi.org/10.1109/icnnsp.2008.4590298.
Pełny tekst źródłaGusciora, George L. "Back Propagation On Warp". W 32nd Annual Technical Symposium, redaktor J. P. Letellier. SPIE, 1989. http://dx.doi.org/10.1117/12.948568.
Pełny tekst źródła"[Back cover]". W 1985 Antennas and Propagation Society. IEEE, 1985. http://dx.doi.org/10.1109/aps.1985.1149404.
Pełny tekst źródła"[Back cover]". W 1986 Antennas and Propagation Society. IEEE, 1986. http://dx.doi.org/10.1109/aps.1986.1149633.
Pełny tekst źródła"[Back cover]". W 1987 Antennas and Propagation Society. IEEE, 1987. http://dx.doi.org/10.1109/aps.1987.1149917.
Pełny tekst źródłaZhang, Xishan, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo i in. "Fixed-Point Back-Propagation Training". W 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00240.
Pełny tekst źródłaHeilpern, T., A. Shlivinski i E. Heyman. "Beam-based back-propagation imaging". W 2009 International Conference on Electromagnetics in Advanced Applications (ICEAA). IEEE, 2009. http://dx.doi.org/10.1109/iceaa.2009.5297549.
Pełny tekst źródłaJurik, M. "Back error propagation - A critique". W COMPCON Spring 88. IEEE, 1988. http://dx.doi.org/10.1109/cmpcon.1988.4895.
Pełny tekst źródłaSon Nguyen, Kanishka Tyagi, Parastoo Kheirkhah i Michael Manry. "Partially affine invariant back propagation". W 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727283.
Pełny tekst źródłaBaum, Eric B. "Generalizing back propagation to computation". W AIP Conference Proceedings Volume 151. AIP, 1986. http://dx.doi.org/10.1063/1.36218.
Pełny tekst źródłaRaporty organizacyjne na temat "Back propagation"
Deller, Jr, Hunt J. R. i S. D. A Simple 'Linearized' Learning Algorithm Which Outperforms Back-Propagation. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1992. http://dx.doi.org/10.21236/ada249697.
Pełny tekst źródłaSt. George, Brett A. Speech Coding and Phoneme Classification Using a Back-Propagation Neural Network. Fort Belvoir, VA: Defense Technical Information Center, maj 1997. http://dx.doi.org/10.21236/ada418472.
Pełny tekst źródłaVanderbeek, Richard G., i Alice M. Harper. Back-Propagation Network for Analog Signal Separation in High Noise Environments. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1992. http://dx.doi.org/10.21236/ada254245.
Pełny tekst źródłaMalik. L51877 Crack Arrest Toughness to Avoid Dynamic Ductile Fracture in Gas Transmission Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), marzec 2001. http://dx.doi.org/10.55274/r0010192.
Pełny tekst źródłaKinnan, Cynthia, Krislert Samphantharak, Robert Townsend i Diego A. Vera-Cossio. Propagation and Insurance in Village Networks. Inter-American Development Bank, lipiec 2022. http://dx.doi.org/10.18235/0004385.
Pełny tekst źródłaMuhlestein, Michael, i Carl Hart. Numerical analysis of weak acoustic shocks in aperiodic array of rigid scatterers. Engineer Research and Development Center (U.S.), październik 2020. http://dx.doi.org/10.21079/11681/38579.
Pełny tekst źródłaWilkins, C. A., i W. A. Sands. Comparison of a Back Propagation Artificial Neural Network Model with a Linear Regression Model for Personnel Selection. Fort Belvoir, VA: Defense Technical Information Center, maj 1994. http://dx.doi.org/10.21236/ada280023.
Pełny tekst źródłaBiagio, Di. L52037 Ductile Fracture Propagation Resistance for Advanced Pipeline Designs. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), styczeń 2008. http://dx.doi.org/10.55274/r0011001.
Pełny tekst źródłaKirichek, Galina, Vladyslav Harkusha, Artur Timenko i Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], luty 2020. http://dx.doi.org/10.31812/123456789/3743.
Pełny tekst źródłaCuadra, Gabriel, i Victoria Nuguer. Research Insights: How Can Macro-Prudential Policy Control the Impact of Cross-Border Bank Flows on Emerging Market Economies? Inter-American Development Bank, czerwiec 2021. http://dx.doi.org/10.18235/0003327.
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