Academic literature on the topic 'Neural network: performance'
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Journal articles on the topic "Neural network: performance"
Panda, Subodh, Bikash Swain, and Sandeep Mishra. "Boiler Performance Optimization Using Process Neural Network." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 298–300. http://dx.doi.org/10.15373/2249555x/july2013/93.
Full textCR, Dhivyaa, Sudhakar R, Nithya K, and Prabhakar E. "Performance Analysis of Convolutional Neural Network for Retinal Image Classification." International Journal of Psychosocial Rehabilitation 23, no. 4 (December 20, 2019): 1149–59. http://dx.doi.org/10.37200/ijpr/v23i4/pr190441.
Full textLi, Xiao Hu, Feng Xu, Jin Hua Zhang, and Su Nan Wang. "A New Small-World Neural Network with its Performance on Fault Tolerance." Advanced Materials Research 629 (December 2012): 719–24. http://dx.doi.org/10.4028/www.scientific.net/amr.629.719.
Full textYen, Gary G., and Haiming Lu. "Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design." International Journal of Computational Intelligence and Applications 03, no. 03 (September 2003): 213–32. http://dx.doi.org/10.1142/s1469026803000975.
Full textJeong, Yeongsang, and Sungshin Kim. "A Study of Arrow Performance using Artificial Neural Network." Journal of Korean Institute of Intelligent Systems 24, no. 5 (October 25, 2014): 548–53. http://dx.doi.org/10.5391/jkiis.2014.24.5.548.
Full textTun, Myat Thida. "Implementation and Performance Evaluation of Neural Network for English Alphabet Recognition System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 474–78. http://dx.doi.org/10.31142/ijtsrd15863.
Full textSuhailayani Suhaimi, Nur, Zalinda Othman, and Mohd Ridzwan Yaakub. "Analyzing Prediction Performance between Wavelet Neural Network and Product-Unit Neural Network." Journal of Physics: Conference Series 1432 (January 2020): 012081. http://dx.doi.org/10.1088/1742-6596/1432/1/012081.
Full textStevens, R., J. Ikeda, A. Casillas, J. Palacio-Cayetano, and S. Clyman. "Artificial neural network-based performance assessments." Computers in Human Behavior 15, no. 3-4 (May 1999): 295–313. http://dx.doi.org/10.1016/s0747-5632(99)00025-4.
Full textJasic, Teo, and Douglas Wood. "Neural network protocols and model performance." Neurocomputing 55, no. 3-4 (October 2003): 747–53. http://dx.doi.org/10.1016/s0925-2312(03)00437-5.
Full textWilson, Charles L., James L. Blue, and Omid M. Omidvar. "Training Dynamics and Neural Network Performance." Neural Networks 10, no. 5 (July 1997): 907–23. http://dx.doi.org/10.1016/s0893-6080(96)00119-0.
Full textDissertations / Theses on the topic "Neural network: performance"
Tupas, Ronald-Ray Tiñana. "Artificial neural network modelling of filtration performance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/MQ59890.pdf.
Full textBataineh, Mohammad Hindi. "Artificial neural network for studying human performance." Thesis, University of Iowa, 2012. https://ir.uiowa.edu/etd/3259.
Full textAlrumah, Muhammad K. "Neural networks predict well inflow performance." Texas A&M University, 2003. http://hdl.handle.net/1969.1/349.
Full textChen, Dong. "Neural network model for predicting performance of projects." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0021/MQ48059.pdf.
Full textSchilling, Glenn D. "Modeling Aircraft Fuel Consumption with a Neural Network." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36533.
Full textMaster of Science
Rosenfeld, Jonathan S. (Jonathan Shmuel). "On the relation between neural network size and performance." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122703.
Full textThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 57-58).
Artificial Neural Networks (NN) are notorious for their size requirements and for the effort involved in developing well performing network models. This thesis uncovers a fundamental relationship that ties model size and performance in a predictable manner. This relationship enables a well-founded development of networks at small scale while producing insight into their large-scale behavior.
by Jonathan S. Rosenfeld.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Mitchell, David. "Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?" Thesis, University of North Texas, 1998. https://digital.library.unt.edu/ark:/67531/metadc278874/.
Full textMamidanna, Pranav. "Optimizing Neural Source Extraction Algorithms: A Performance Measure Based on Neuronal Network Properties." Thesis, KTH, Numerisk analys, NA, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210052.
Full textExtraktion av neuronal aktivitet från elektrofysiologiska och kalciumavbildningsmätningar utgör ett viktigt problem inom neurovetenskapen. Alla existerande automatiska algoritmer för detta ändamål beror dock i dagsläget på manuell handpåläggning och parameterinställning. I detta examensarbete presenterar vi ett nytt prestandamått baserat på välgrundade begrepp rörande organisationen av neuronala nätverk. Detta möjliggör en systematisk parameterinställning genom att använda tekniker från statistisk experimentdesign och response surface-metoder. Vi har implementerat detta ramverk för en algoritm som används för att extrahera neuronal aktivitet från mikroendoskopisk kalciumavbildningsdata och visar att detta förfarande avsevärt minskar behovet av manuell inblandning.
Nichols, Roger Alan. "A performance baseline for machinery condition classification by neural network." Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03172010-020117/.
Full textLin, Yu Chu. "E-government website performance evaluation based on BP neural network." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691489.
Full textBooks on the topic "Neural network: performance"
A, Lloyd J. Performance and scalability of neural network implementations on parallel computers. Manchester: UMIST, 1995.
Find full textKobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.
Find full textKobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.
Find full textKobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.
Find full textKobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.
Find full textMcGrath, M. Neural networks for financial performance prediction. Dublin: University CollegeDublin, 1995.
Find full text1941-, Venetsanopoulos A. N., ed. Artificial neural networks: Learning algorithms, performance evaluation, and applications. Boston: Kluwer Academic, 1993.
Find full textSimon, Donald L. Adaptive optimization of aircraft engine performance using neural networks. [Washington, D.C.]: National Aeronautics and Space Administration, 1995.
Find full textRutkowski, Leszek. Flexible neuro-fuzzy systems: Structures, learning, and performance evaluation. Boston: Kluwer Academic Publishers, 2004.
Find full textPiramuthu, Selwyn. Using feature construction to improve the performance of neural networks. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign., 1993.
Find full textBook chapters on the topic "Neural network: performance"
Feng, Ruibin, Chi-Sing Leung, Kai-Tat Ng, and John Sum. "The Performance of the Stochastic DNN-kWTA Network." In Neural Information Processing, 279–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_35.
Full textSuddarth, S. C., and Y. L. Kergosien. "Rule-injection hints as a means of improving network performance and learning time." In Neural Networks, 120–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7_33.
Full textRaeth, Peter G. "An Experiment with 3-D Surface Maps to Illustrate Neural Network Performance." In International Neural Network Conference, 733–37. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_63.
Full textPomerleau, Dean A. "Driving Results and Performance." In Neural Network Perception for Mobile Robot Guidance, 71–83. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3192-0_5.
Full textKamimura, Ryotaro. "Experimental Analysis of Performance of Temporal Supervised Learning Algorithm, Applied to a Long and Complex Sequence." In International Neural Network Conference, 753–56. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_67.
Full textUeguchi, Taisei, Nobuyuki Matsui, and Teijiro Isokawa. "Performance of Qubit Neural Network in Chaotic Time Series Forecasting." In Neural Information Processing, 253–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_28.
Full textYao, Kai, Kaizhu Huang, Rui Zhang, and Amir Hussain. "Improving Deep Neural Network Performance with Kernelized Min-Max Objective." In Neural Information Processing, 182–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_17.
Full textFernández, Benito R. "Performance analysis of artificial neural network methods." In Artificial Neural Networks for Intelligent Manufacturing, 299–368. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0713-6_12.
Full textZhao, Jieyu, and John Shawe-Taylor. "Neural Network Optimization for Good Generalization Performance." In ICANN ’94, 561–64. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2097-1_131.
Full textKechadi, M.-Tahar. "Recurrent neural network approach for partitioning irregular graphs." In High-Performance Computing and Networking, 450–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100606.
Full textConference papers on the topic "Neural network: performance"
Ghorbanian, Kaveh, and Mohammad Gholamrezaei. "Axial Compressor Performance Map Prediction Using Artificial Neural Network." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27165.
Full textChou, Hung, Andrew A. Kostrzewski, Shudong Wu, Freddie S. Lin, and Thomas T. Lu. "Performance evaluation of a holographic optical neural network system." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983187.
Full textKepner, Jeremy, Simon Alford, Vijay Gadepally, Michael Jones, Lauren Milechin, Albert Reuther, Ryan Robinett, and Sid Samsi. "GraphChallenge.org Sparse Deep Neural Network Performance." In 2020 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2020. http://dx.doi.org/10.1109/hpec43674.2020.9286253.
Full textMehdizadeh, Nasser S., Payam Sinaei, and Ali L. Nichkoohi. "Modeling Jones’ Reduced Chemical Mechanism of Methane Combustion With Artificial Neural Network." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31186.
Full textJoung, Junegak, and Harrison M. Kim. "Importance-Performance Analysis of Product Attributes Using Explainable Deep Neural Network From Online Reviews." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22382.
Full textBiswas, M. A. Rafe, and Melvin D. Robinson. "Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51723.
Full textShiflett, Kyle, Dylan Wright, Avinash Karanth, and Ahmed Louri. "PIXEL: Photonic Neural Network Accelerator." In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2020. http://dx.doi.org/10.1109/hpca47549.2020.00046.
Full textKepner, Jeremy, Vikalo Gadepally, Hayden Jananthan, Lauren Milechin, and Sid Samsi. "Sparse Deep Neural Network Exact Solutions." In 2018 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2018. http://dx.doi.org/10.1109/hpec.2018.8547742.
Full textKepner, Jeremy, Simon Alford, Vijay Gadepally, Michael Jones, Lauren Milechin, Ryan Robinett, and Sid Samsi. "Sparse Deep Neural Network Graph Challenge." In 2019 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2019. http://dx.doi.org/10.1109/hpec.2019.8916336.
Full textSamsi, Siddharth, Andrew Prout, Michael Jones, Andrew Kirby, Bill Arcand, Bill Bergeron, David Bestor, et al. "Benchmarking network fabrics for data distributed training of deep neural networks." In 2020 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2020. http://dx.doi.org/10.1109/hpec43674.2020.9286232.
Full textReports on the topic "Neural network: performance"
Fine, Terrence L., and Thomas W. Parks. Statistical Benchmarks for Neural Network Performance. Fort Belvoir, VA: Defense Technical Information Center, October 1992. http://dx.doi.org/10.21236/ada294937.
Full textFix, Edward L. Neural Network Based Human Performance Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada229822.
Full textWilson, Charles L., James L. Blue, and Omid M. Omidvar. The effect of training dynamics on neural network performance. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5696.
Full textWilson, Charles L., James L. Blue, and Omid M. Omidvar. Improving neural network performance for character and fingerprint classification by altering network dynamics. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5695.
Full textYu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.
Full textRose-Pehrsson, Susan, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, and Mark T. Wright. Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383627.
Full textField, R. L., E. J. Yoerger, and P. K. Simpson. Performance of Neural Networks in Classifying Environmentally Distorted Transient Signals. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada230739.
Full textPuttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.
Full textKozman, Robert, and Walter J. Freeman. The Effect of External and Internal Noise on the Performance of Chaotic Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada413501.
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