姓 名：高飞

学 位：博 士

职 称：教 授

研究方向：分数阶系统、大数据分析、神经网络、机器学习、群智能算法等

亚博888的联系方式：

电话：18971097697

个人亚博888主页：

1.  简介

高飞 博士，武汉理工大学理学院数学系应用数学专业教授（2013.9-），居里夫人marie-curie fellow，硕士生导师，归国留学博士后、博士后出站。从事涉及神经网络、机器学习、分数阶系统、群智能算法等领域的科学研究工作，并将它们应用于各种实际问题。

 2.  研究生招生

数学专业：应用数学、基础数学——机器学习、分数阶偏微分方程、大数据分析、神经网络、gpt与大语言模型、社交网络、无人机应用、混沌系统、随机微分方程、最优化理论与方法、

统计专业（学硕及专硕）：大数据分析、神经网络、机器学习、gpt与大语言模型、社交网络、无人机应用、混沌系统、随机微分方程、数理统计学相关

高飞-亚博888

高飞博士在国内外重要学术期刊及高水平国际会议上发表论文60 篇，30 篇被三大检索收录。获得韩国政府bk21奖学金(2008.3-2009.3)和欧盟第七框架项目(2011.11-2012.10)资助，分别在韩国高等科学与技术研究院未来超越人类智能实验室、kaist电气工程与计算机科学系、挪威科技大学ntnu电子与电信系从事计算智能及智能控制的博士后研究工作。

近年来参与国家自然科学基金重点项目（2014-2016）和面上项目（2013-2016）各一项；主持完成国家自然科学基金1项(2007)，中国博士后基金1项(2008-2009)，主持湖北省自然科学基金2项(2009-2011，2018-2019)。

国家精品课程<经济数学——高等数学b>主讲教师，自2002年以来进行高等数学a、b（含双语、纯英文教学）、微积分、概率统计、线性代数、复变函数与积分变换、最优化理论与方法、常微分方程、数值计算等课程的教学与研究。

2006.6毕业于武汉理工大学获博士学位；

2002.6毕业于武汉大学数学与统计学院应用数学专业，获理学硕士学位，获得“武汉大学数学学子奖”，并于2005年获得获湖北省优秀硕士论文；

1999.6在武汉大学数学与计算机科学学院获理学学士学位。

2013/09 - 至今，武汉理工大学，理学院数学系，教授

2011/11–2012/10，挪威科技大学(ntnu)，信息科技与数学及电子工程学院， marie curie fellow博士后(欧盟marie curie cofund项目资助)

2008/03–2009/03，韩国科学技术研究院(kaist)，电子系，博士后(韩国brain korea 21 century项目资助)

2007/12 - 2013/09，武汉理工大学，理学院数学系，副教授

2006/11–2009/07，武汉理工大学，建筑学院，博士后

2004/11 - 2007/11，武汉理工大学，理学院数学系，讲师

1、湖北省自然科学基金项目，2014cfb865、分数阶超混沌的非lyapunov重构研究、2015/01-2016/12、3万、结题、主持

2、国家自然科学基金重大研究计划项目，91324201、非常规突发事件下社会群体心理与行为变化规律和机制、2014/01-2016/12、175万、结题、参与

3、“marie cofund of the european commission - abcde 项目，欧盟n°246016、mathematical analysis on bio-inspired communication network theory、2011/11-2012/10、1万欧元、结题、主持

4、教育部(中国)留学科研启动基金项目，20111j0032、基于量子细菌趋化算法的非lyapunov分析方法研究、2010/06-2011/12、3万、已结题、主持7.  论文（60 ）

[1]    xu y, gao f. a novel higher-order deffuant–weisbuch networks model incorporating the susceptible infected recovered framework[j]. chaos, solitons & fractals, 2024, 182: 114778.

[2]    xie x, gao f. the delayed effect of multiplicative noise on the blow-up for a class of fractional stochastic differential equations[j]. fractal and fractional, 2024, 8(3): 127.

[1]  zhang m, gao f, yang w, zhang h. wildlife object detection method applying segmentation gradient flow and feature dimensionality reduction [j]. electronics, 2023, 12(2).

[2]  zhang m, gao f, yang w, zhang h. real-time target detection system for animals based on self-attention improvement and feature extraction optimization [j]. applied sciences-basel, 2023, 13(6).

[3]  gao f, zhan h. boundedness and exponential stabilization for time–space fractional parabolic–elliptic keller–segel model in higher dimensions [j]. applied mathematics letters, 2023, 144: 108699.

[4]  guo l, gao f, zhan h. existence, uniqueness and l8-bound for weak solutions of a time fractional keller-segel system [j]. chaos solitons & fractals, 2022, 160.

[5]  zhou x, gao f, fang x, lan z. improved bat algorithm for uav path planning in three-dimensional space [j]. ieee access, 2021, 9: 20100-16.

[6]  gao f, li x, li w, zhou x. stability analysis of a fractional-order novel hepatitis b virus model with immune delay based on caputo-fabrizio derivative [j]. chaos solitons & fractals, 2021, 142.

[7]  gao f, li w-q, tong h-q, li x-l. chaotic analysis of atangana-baleanu derivative fractional order willis aneurysm system [j]. chinese physics b, 2019, 28(9).

[8]  zhang j, gao f, chen y, et al. parameter identification of fractional-order chaotic system based on chemical reaction optimization; proceedings of the 2nd international conference on management engineering, software engineering and service sciences (icmss), wuhan, peoples r china, f 2018, jan 13-15, 2018 [c]. 2018.

[9]  gao f, hu d-n, tong h-q, wang c-m. chaotic analysis of fractional willis delayed aneurysm system [j]. acta physica sinica, 2018, 67(15).

[10] mao w, gao f, dong y, li w. a novel paradigm for calculating ramsey number via artificial bee colony algorithm; proceedings of the 35th chinese control conference (ccc), chengdu, peoples r china, f 2016, jul 27-29, 2016 [c]. 2016.

[11] gao f, li t, tong h-q, ou z-l. chaotic dynamics of the fractional willis aneurysm system and its control [j]. acta physica sinica, 2016, 65(23).

[12] gao f, lee t, cao w-j, et al. self-evolution of hyper fractional order chaos driven by a novel approach through genetic programming [j]. expert systems with applications, 2016, 52: 1-15.

[13] gao f, lee x-j, fei f-x, et al. identification time-delayed fractional order chaos with functional extrema model via differential evolution [j]. expert systems with applications, 2014, 41(4): 1601-8.

[14] gao f, fei f-x, lee x-j, et al. inversion mechanism with functional extrema model for identification incommensurate and hyper fractional chaos via differential evolution [j]. expert systems with applications, 2014, 41(4): 1915-27.

[15] gao f, lee x-j, tong h-q, et al. identification of unknown parameters and orders via cuckoo search oriented statistically by differential evolution for noncommensurate fractional-order chaotic systems [j]. abstract and applied analysis, 2013.

[16] gao f, lee x-j, fei f-x, et al. parameter identification for van der pol-duffing oscillator by a novel artificial bee colony algorithm with differential evolution operators [j]. applied mathematics and computation, 2013, 222: 132-44.

[17] gao f, fei f-x, tong h-q, et al. bacterial foraging optimization oriented by atomized feature cloud model strategy; proceedings of the 32nd chinese control conference (ccc), xian, peoples r china, f 2013, jul 26-28, 2013 [c]. 2013.

[18] gao f, qi y, balasingham i, et al. a novel non-lyapunov way for detecting uncertain parameters of chaos system with random noises [j]. expert systems with applications, 2012, 39(2): 1779-83.

[19] gao f, fei f-x, xu q, et al. a novel artificial bee colony algorithm with space contraction for unknown parameters identification and time-delays of chaotic systems [j]. applied mathematics and computation, 2012, 219(2): 552-68.

[20] gao f, fei f-x, deng y-f, et al. a novel non-lyapunov approach through artificial bee colony algorithm for detecting unstable periodic orbits with high orders [j]. expert systems with applications, 2012, 39(16): 12389-97.

[21] xiao j-q, wu m, gao f. divergence points of self-similar measures satisfying the osc [j]. journal of mathematical analysis and applications, 2011, 379(2): 834-41.

[22] gao f, qi y, yin q, xiao j. solving problems in chaos control though an differential evolution algorithm with region zooming; proceedings of the 2nd international conference on mechanical and aerospace engineering (icmae 2011), bangkok, thailand, f 2012, jul 29-31, 2011 [c]. 2012.

[23] gao f, lee j-j, li z, et al. parameter estimation for chaotic system with initial random noises by particle swarm optimization [j]. chaos solitons & fractals, 2009, 42(2): 1286-91.

[24] gao f, gao h, li z, et al. detecting unstable periodic orbits of nonlinear mappings by a novel quantum-behaved particle swarm optimization non-lyapunov way [j]. chaos solitons & fractals, 2009, 42(4): 2450-63.

[25] gao f, li z-q, tong h-q. parameters estimation online for lorenz system by a novel quantum-behaved particle swarm optimization [j]. chinese physics b, 2008, 17(4): 1196-201.

[26] gao f, lee j-j, ieee. a new approach in synchronization of uncertain chaos systems through particle swarm optimization; proceedings of the 6th ieee international conference on industrial informatics, daejeon, south korea, f 2008, jul 13-16, 2008 [c]. 2008.

[27] gao f, lee j-j, ieee. a new approach in discrete chaos system control by differential evolution algorithm; proceedings of the 6th ieee international conference on industrial informatics, daejeon, south korea, f 2008, jul 13-16, 2008 [c]. 2008.

[28] gao f, tong h q. parameter estimation for chaotic system based on particle swarm optimization [j]. acta physica sinica, 2006, 55(2): 577-82.

[29] gao f, tong h q. a novel optimal pid tuning and on-line tuning based on particle swarm optimization; proceedings of the international conference on sensing, computing and automation, chongqing, peoples r china, f dec 2006, may 08-11, 2006 [c]. 2006.

[30] gao f, tong h, ieee. control a novel discrete chaotic system through particle swarm optimization; proceedings of the 6th world congress on intelligent control and automation, dalian, peoples r china, f 2006, jun 21-23, 2006 [c]. 2006.

[31] gao f, tong h. ueas: a novel united evolutionary algorithm scheme [m]//king i, wang j, chan l, wang d l. neural information processing, pt 3, proceedings. 2006: 772-80.

[32] gao f, tong h. differential evolution: an efficient method in optimal pid tuning and on-line tuning [j]. dynamics of continuous discrete and impulsive systems-series b-applications & algorithms, 2006, 13: 785-9.

[33] gao f, tong h. particle swarm optimization: an efficient method for tracing periodic orbits and controlling chaos [j]. dynamics of continuous discrete and impulsive systems-series b-applications & algorithms, 2006, 13: 780-4.