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Original Articles

Active Adaptive Combustion Control Using Neural Networks

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Pages 25-47 | Received 27 Jul 1998, Accepted 05 Oct 1999, Published online: 27 Apr 2007

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Long Zhang, Xingyu Su, Hua Zhou & Zhuyin Ren. (2023) Sliding mode control for longitudinal oscillating combustion. Combustion Theory and Modelling 27:5, pages 653-684.
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Long Zhang, Shan Li, Yuan Xue, Hua Zhou & Zhuyin Ren. (2022) Neural network PID control for combustion instability. Combustion Theory and Modelling 26:2, pages 383-398.
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T. Marwala, M. Lagazio & T. Tettey. (2009) An Integrated Human–Computer System for Controlling Interstate Disputes. International Journal of Computers and Applications 31:4, pages 239-246.
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Articles from other publishers (17)

Dan Zhao. 2023. Thermoacoustic Combustion Instability Control. Thermoacoustic Combustion Instability Control 443 512 .
Long Zhang, Xingyu Su, Hua Zhou, Xiangyang Wang & Zhuyin Ren. (2022) Active Control of Multiple Neural Networks for Oscillating Combustion. AIAA Journal 60:6, pages 3821-3833.
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Seungtaek Oh, Jaehyeon Kim & Yongmo Kim. (2018) Analysis for Combustion Instability and Stabilization Characteristics in a Swirled Premixed Combustor With a Slotted Plate. Journal of Engineering for Gas Turbines and Power 140:9.
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Dan Zhao, Zhengli Lu, He Zhao, X.Y. Li, Bing Wang & Peijin Liu. (2018) A review of active control approaches in stabilizing combustion systems in aerospace industry. Progress in Aerospace Sciences 97, pages 35-60.
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Stefan Jaensch & Wolfgang Polifke. (2017) Uncertainty encountered when modelling self-excited thermoacoustic oscillations with artificial neural networks. International Journal of Spray and Combustion Dynamics 9:4, pages 367-379.
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Tshilidzi Marwala & Monica LagazioTshilidzi Marwala & Monica Lagazio. 2011. Militarized Conflict Modeling Using Computational Intelligence. Militarized Conflict Modeling Using Computational Intelligence 217 244 .
N. Hansen, A.S.P. Niederberger, L. Guzzella & P. Koumoutsakos. (2009) A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion. IEEE Transactions on Evolutionary Computation 13:1, pages 180-197.
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Dan-Ping Sun, Qing-Yan Fang, Hua-Jian Wang & Huai-Chun Zhou. (2008) A compact optimization strategy for combustion in a 125 MW tangentially anthracite-fired boiler by an artificial neural network. Asia-Pacific Journal of Chemical Engineering 3:4, pages 432-439.
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Danping Sun, Qingyan Fang, Huajian Wang & Huaichun Zhou. 2007. Challenges of Power Engineering and Environment. Challenges of Power Engineering and Environment 644 651 .
Rudibert King, Ralf Becker, Gerrit Feuerbach, Lars Henning, Ralf Petz, Wolfgang Nitsche, Olaf Lemke & Wolfgang Neise. (2006) Adaptive flow control using slope seeking. Adaptive flow control using slope seeking.
Nihat Yildiz. (2005) Layered feedforward neural network is relevant to empirical physical formula construction: A theoretical analysis and some simulation results. Physics Letters A 345:1-3, pages 69-87.
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Ralf Becker, Maiko Garwon, Carsten Gutknecht, Günter Bärwolff & Rudibert King. (2005) Robust control of separated shear flows in simulation and experiment. Journal of Process Control 15:6, pages 691-700.
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T. Marwala & M. Lagazio. (2004) Modeling and controlling interstate conflict. Modeling and controlling interstate conflict.
Soteris A. Kalogirou. (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science 29:6, pages 515-566.
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Byeong-Jun Lee & D.A. Santavicca. (2002) Pre-detection Parameter of the Combustion Instabilities in the Gas Turbine Combustor. Transactions of the Korean Society of Mechanical Engineers B 26:5, pages 750-756.
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R. Becker, M. Garwon, C. Gutknecht, G. Bärwolff & R. King. (2002) Regelung aerodynamischer Strömungen am Beispiel einer rückwärts gewandten Stufe (Robust Control of a Backward-Facing Step Flow). auto 50:2, pages 79.
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R. Blonbou, A. Laverdant, S. Zaleski & P. Kuentzmann. (2000) Active control of combustion instabilities on a rijke tube using neural networks. Proceedings of the Combustion Institute 28:1, pages 747-755.
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