貴州大學(xué)計算機科學(xué)與信息學(xué)院導(dǎo)師：龍飛 正文

　　龍飛，男，博士，教授。畢業(yè)于東南大學(xué)。貴州大學(xué)智能信息處理研究所所長，碩士生指導(dǎo)教師。

　　研究方向
　　包含混雜系統(tǒng)控制及應(yīng)用、神經(jīng)網(wǎng)絡(luò)控制、圖象處理、智能天線、數(shù)字與通信和智能信息處理等相關(guān)領(lǐng)域。

　　科研情況
　　在混雜系統(tǒng)和神經(jīng)網(wǎng)絡(luò)控制方面進行了深入而全面的研究，得到一系列的結(jié)果。部分結(jié)果發(fā)表在 Journal of Dynamic Systems, Measurement and Control, ASME Transaction、Neurocomputing、ACTA Journal---Control and Intelligent Systems、Nonlinear Analysis: Hybrid Systems、Dynamics of Continuous Discrete andImpulsive Systems、Lecture Notes in Computer Science 和 International Journal of Innovative Computing, Information and Control等國際學(xué)術(shù)期刊上。目前受邀作為一些國際學(xué)術(shù)期刊（IEEE Transaction On Automatic Control, IEEE Transaction On Aerospace and Electronic Systems, IEEE Transaction On SMC (part B), Nonlinear Analysis: Hybrid Systems, Control and Intelligent Systems, International Journal of Systems and Science, Journal of Control Theory and Application 等）和國際會議（IEEE CDC, ACC, CCC等）的審稿人?，F(xiàn)為美國電子與電氣工程師協(xié)會會員（IEEE Member），中國自動化學(xué)會青年工作委員會委員，入選2007年度中科院“西部之光”人才培養(yǎng)計劃，國家自然科學(xué)基金項目評審專家。被聘為國際會議ISCAA 2010 和CCC 2011 的國際程序委員會委員。

　　科研項目
　　主持承擔(dān)國家自然科學(xué)基金3項、中科院“西部之光”人才培養(yǎng)計劃項目1項、教育部科技重點項目1項、貴州省農(nóng)業(yè)攻關(guān)項目1項、貴州省科學(xué)基金2項及貴州大學(xué)人才引進基金1項。

　　發(fā)表論文
　　已累計在國內(nèi)外學(xué)術(shù)刊物和學(xué)術(shù)會議上公開發(fā)表或錄用學(xué)術(shù)論文40余篇，其中SCI收錄8篇，SCI雜志錄用4篇，EI收錄20余篇。主要代表作：
　　 [1] Lei Yu, Shumin Fei and Fei Long, Multilayer neural networks-based direct adaptive control for switched nonlinear systems, Neurocomputing, Accepted, July, 2010.
　　 [2] Fei Long and Changlin Li, Disturbance Attenuation for a Class of Discrete-time Switched Systems with Exponential Uncertainty, Journal of the Korean Mathematical Society, Accepted, August, 2010.
　　 [3] Fei Long, Changlin Li and Changzheng Cui, Dynamic Output Feedback Control for a Class of Switched Linear Systems with Exponential Uncertainty, International Journal of Innovative Computing, Information and Control, Vol. 6, No. 4, pp. 1727-1736, April 2010.
　　 [4] Changzheng Cui, Fei Long and Changlin Li, Disturbance Attenuation for Switched System with Continuous-Time and Discrete-Time Subsystems: State Feedback Case, ICIC Express Letters, Vol. 4, Issue 1, pp. 205-212, 2010.
　　 [5] Jinxin Lin, Shumin Fei, Jie Shen and Fei Long, Exponential estimates and H-infinity control for singular Markovian jump systems with interval time-varying delay, Proceedings of The Institution of Mechanical Engineers Part I-Journal Of Systems and Control Engineering, 224 (I4): 437-456 2010.
　　 [6] Fei Long, Changlin Li and Changzheng Cui, Robust Stabilization and Disturbance Rejection for a Class of Hybrid Linear Systems Subject to Exponential Uncertainty, Journal of Dynamic Systems, Measurement and Control, ASME Transaction, Vol. 131, Art. No. 034501, pp. 1-7, March, 2009
　　 [7] Fei Long, Shumin Fei, Zhumu Fu, Shiyou Zheng and Wei Wei, Control and quadratic stabilization of switched linear systems with linear fractional uncertainties via output feedback, Nonlinear Analysis: Hybrid Systems, Vol. 2, No. 1, pp. 18–27, 2008.
　　 [8] Fei Long, Shumin Fei and Shiyou Zheng, State Feedback Singular Control for Switched Linear Systems, ACTA Journal---Control and Intelligent Systems, Vol. 36, No. 1, pp. 17-26, 2008.
　　 [9] Fei Long, S. M. Fei, Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems, Neurocomputing, Vol. 71, Issues 7-9, March, pp. 1741-1747, 2008.
　　 [10] Fei Long, Sheng Zhang and Jun Yang, Backstepping Stabilization For A Class Of SISO Switched Nonlinear Systems With Trigonal Structure, International Journal of Computer Science and Network Security, Vol. 7, No. 12, pp. 110-113, 2007.
　　 [11] Fei Long and Shumin Fei, Robust State Feedback Control for Uncertain Nonlinear Switched Systems: Nonlinear Matrix Inequality Approach, Dynamics of Continuous Discrete and Impulsive Systems---Series A: Mathematical Analysis 13: 172-178, Part 1, Suppl. S, Dec. 2006.
　　 [12] Fei Long, Shumin Fei and Shiyou Zheng, Adaptive H-infinity Control For a Class of Switched Nonlinear Systems Based on RBF Neural Networks, International Journal of Hybrid Systems, Vol. 4, No. 3 & 4, pp. 369-381, 2004.
　　　

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