鏀硅繘铦磋澏浼樺寲绠楁硶鍦?qar 鏁版嵁閲嶆瀯涓殑搴旂敤-mg网址

涓浗姘戣埅澶у瀛︽姤鈥衡€?, 鈥衡€?: 22-27.

鈥?姘戠敤鑸┖ 鈥?/span>

鏀硅繘铦磋澏浼樺寲绠楁硶鍦?qar 鏁版嵁閲嶆瀯涓殑搴旂敤

寮?楣?nbsp; 锛屽垬璧熻穬锛屾鐓ф枌

(涓浗姘戣埅澶у . 閫傝埅瀛﹂櫌锛? 鐢靛瓙淇℃伅涓庤嚜鍔ㄥ寲瀛﹂櫌锛? 宸ョ▼鎶€鏈缁冧腑蹇冿紝澶╂触 300300)

鏀剁鏃ユ湡:2021-02-15 淇洖鏃ユ湡:2021-02-15 鎺ュ彈鏃ユ湡:2020-11-30 鍑虹増鏃ユ湡:2021-10-20 鍙戝竷鏃ユ湡:2021-10-31
浣滆€呯畝浠?寮犻箯锛?963鈥旓級锛岀敺锛屽寳浜汉锛屾暀鎺堬紝纭曞＋锛岀爺绌舵柟鍚戜负椋炴満鏅鸿兘鏁呴殰璇婃柇銆?

application of improved butterfly optimization algorithm in qar data reconstruction

zhang peng , liu y unyue 锛孌uan zhaobin

(a. college of airworthiness; b. college of electronic information and automation; c. engineering techniques training center, cauc, tianjin 300300, china)

received:2021-02-15 revised:2021-02-15 accepted:2020-11-30 online:2021-10-20 published:2021-10-31

鎽樿锛?

閽堝蹇€熷瓨鍙栬褰曞櫒锛?span>qar锛?span>quick access recorder锛夋暟鎹腑鍥犳祴閲忎紶鎰熷櫒瀛樺湪绯荤粺璇樊鑰屽鑷?span>qar 鏁版嵁涓嶆弧瓒虫暟鎹浉瀹规€х殑闂锛岄噰鐢ㄨ澊铦朵紭鍖栫畻娉曞強鏈€灏忎簩涔樺噯鍒欏皢绯荤粺璇樊鐨勫弬鏁颁及璁￠棶棰樿浆鎹负瑙ｇ┖闂翠笂鐨勫嚱鏁颁紭鍖栭棶棰樸€備负鎻愰珮铦磋澏浼樺寲绠楁硶鐨勬€ц兘锛屽埄鐢ㄦ贩娌屾槧灏勫绉嶇兢杩涜鍒濆鍖栵紝鍔ㄦ€佽皟鏁磋浆鎹㈡鐜囧绠楁硶杩涜鏀硅繘锛屽皢璇ユ柟娉曞簲鐢ㄤ簬椋炶濮挎€佽鍙婅閫熷害 qar 鏁版嵁鐨勪慨姝ｅ強閲嶆瀯涓€傚疄楠岀粨鏋滆〃鏄庯紝璇ユ柟娉曠畝鍗曞彲琛岋紝鏁版嵁閲嶆瀯绮惧害杈冮珮锛屾弧瓒虫暟鎹浉瀹规€ц姹傘€?span>

寮?楣? 鍒樿禑璺? 娈电収鏂?. 鏀硅繘铦磋澏浼樺寲绠楁硶鍦?qar 鏁版嵁閲嶆瀯涓殑搴旂敤[j]. 涓浗姘戣埅澶у瀛︽姤, 2021, 39(5): 22-27.

zhang peng , liu y unyue , duan zhaobin. application of improved butterfly optimization algorithm in qar data reconstruction[j]. journal of civil aviation university of china, 2021, 39(5): 22-27.

[1] 涓浗浜烘皯瑙ｆ斁鍐涙€昏澶囬儴鍐涗簨璁粌鏁欐潗缂栬緫宸ヤ綔濮斿憳浼? 椋炶鍣?绯荤粺杈ㄨ瘑瀛m]. 鍖椾含: 鍥介槻宸ヤ笟鍑虹増绀? 2003.

[2] 绋?棰? 闉?骞? 鍚?宄? 璐熻嵎妯″瀷鍙傛暟杈ㄨ瘑鐨勭矑瀛愮兢浼樺寲娉曞強鍏?涓庡熀鍥犵畻娉曟瘮杈僛j]. 鐢靛姏绯荤粺鑷姩鍖? 2003, 27(11): 25-29.

[3] 浠诲瓙姝? 浼?鍐? 鑷€傚簲閬椾紶绠楁硶鐨勬敼杩涘強鍦ㄧ郴缁熻鲸璇嗕腑搴旂敤鐮旂┒ [j]. 绯荤粺浠跨湡瀛︽姤, 2006, 18(1): 41-43, 66.

[4] 鍒橀噾鐞? 娌堟檽钃? 璧?榫? 绯荤粺杈ㄨ瘑鐞嗚鍙?matlab 浠跨湡[m]. 鍖椾含锛?鐢靛瓙宸ヤ笟鍑虹増绀? 2013.

[5] arora s, singh s. butterfly optimization algorithm: a novel approach for global optimization[j]. soft computing, 2019, 23(3): 715-734.

[6] arora s, singh s. butterfly algorithm with levy flights for global optimization[c]//2015 international conference on signal processing, computing and control (ispcc), september 24-26, 2015, waknaghat, india. ieee, 2015: 220-224.

[7] arora s, singh s. an improved butterfly optimization algorithm for global optimization[j]. advanced science, engineering and medicine, 2016, 8(9):711-717.

[8] arora s, singh s. an effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization[j]. international journal of interactive multimedia and artificial intelligence, 2017, 4(4): 14.

[9] kazimipour b, li x d, qin a k. a review of population initialization techniques for evolutionary algorithms[c]//2014 ieee congress on evolutionary computation (cec), july 6-11, 2014, beijing, china. ieee, 2014:2585-2592.

[10] 宕旈洩濠? 鏉?棰? 鑼冨槈璞? 鍏ㄥ眬娣锋矊铦欒潬浼樺寲绠楁硶[j]. 涓滃寳澶у瀛︽姤 (鑷劧绉戝鐗?, 2020, 41(4): 488-491, 498.

[11] 鍚存．鍫? 椋炶鎺у埗绯荤粺[m]. 2 鐗? 鍖椾含: 鍖椾含鑸┖鑸ぉ澶у鍑虹増绀? 2013.

[12] klein v, morelli e a. aircraft system identification: theory and practice[m]. reston: american institute of aeronautics and astronau鍘?tics, 2006.

[13] 寮犱笘寮? 鏇茬嚎鍥炲綊鐨勬嫙鍚堜紭搴︽寚鏍囩殑鎺㈣[j]. 涓浗鍗敓缁熻, 2002, 19(1): 9-11.

[14] 璋?鍏? 楂樹笢绾? 闈炵嚎鎬у洖褰掓柟娉曠殑搴旂敤涓庢瘮杈僛j]. 鏁板鐨勫疄璺典笌璁?璇? 2009, 39(10): 117-121.

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鎽樿

鏀硅繘铦磋澏浼樺寲绠楁硶鍦?qar 鏁版嵁閲嶆瀯涓殑搴旂敤-mg网址

鏀硅繘铦磋澏浼樺寲绠楁硶鍦?qar 鏁版嵁閲嶆瀯涓殑搴旂敤

application of improved butterfly optimization algorithm in qar data reconstruction

寮曠敤鏈枃

浣跨敤鏈枃

[1]	涓浗浜烘皯瑙ｆ斁鍐涙€昏澶囬儴鍐涗簨璁粌鏁欐潗缂栬緫宸ヤ綔濮斿憳浼? 椋炶鍣?绯荤粺杈ㄨ瘑瀛m]. 鍖椾含: 鍥介槻宸ヤ笟鍑虹増绀? 2003.
[2]	绋?棰? 闉?骞? 鍚?宄? 璐熻嵎妯″瀷鍙傛暟杈ㄨ瘑鐨勭矑瀛愮兢浼樺寲娉曞強鍏?涓庡熀鍥犵畻娉曟瘮杈僛j]. 鐢靛姏绯荤粺鑷姩鍖? 2003, 27(11): 25-29.
[3]	浠诲瓙姝? 浼?鍐? 鑷€傚簲閬椾紶绠楁硶鐨勬敼杩涘強鍦ㄧ郴缁熻鲸璇嗕腑搴旂敤鐮旂┒ [j]. 绯荤粺浠跨湡瀛︽姤, 2006, 18(1): 41-43, 66.
[4]	鍒橀噾鐞? 娌堟檽钃? 璧?榫? 绯荤粺杈ㄨ瘑鐞嗚鍙?matlab 浠跨湡[m]. 鍖椾含锛?鐢靛瓙宸ヤ笟鍑虹増绀? 2013.
[5]	arora s, singh s. butterfly optimization algorithm: a novel approach for global optimization[j]. soft computing, 2019, 23(3): 715-734.
[6]	arora s, singh s. butterfly algorithm with levy flights for global optimization[c]//2015 international conference on signal processing, computing and control (ispcc), september 24-26, 2015, waknaghat, india. ieee, 2015: 220-224.
[7]	arora s, singh s. an improved butterfly optimization algorithm for global optimization[j]. advanced science, engineering and medicine, 2016, 8(9):711-717.
[8]	arora s, singh s. an effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization[j]. international journal of interactive multimedia and artificial intelligence, 2017, 4(4): 14.
[9]	kazimipour b, li x d, qin a k. a review of population initialization techniques for evolutionary algorithms[c]//2014 ieee congress on evolutionary computation (cec), july 6-11, 2014, beijing, china. ieee, 2014:2585-2592.
[10]	宕旈洩濠? 鏉?棰? 鑼冨槈璞? 鍏ㄥ眬娣锋矊铦欒潬浼樺寲绠楁硶[j]. 涓滃寳澶у瀛︽姤 (鑷劧绉戝鐗?, 2020, 41(4): 488-491, 498.
[11]	鍚存．鍫? 椋炶鎺у埗绯荤粺[m]. 2 鐗? 鍖椾含: 鍖椾含鑸┖鑸ぉ澶у鍑虹増绀? 2013.
[12]	klein v, morelli e a. aircraft system identification: theory and practice[m]. reston: american institute of aeronautics and astronau鍘?tics, 2006.
[13]	寮犱笘寮? 鏇茬嚎鍥炲綊鐨勬嫙鍚堜紭搴︽寚鏍囩殑鎺㈣[j]. 涓浗鍗敓缁熻, 2002, 19(1): 9-11.
[14]	璋?鍏? 楂樹笢绾? 闈炵嚎鎬у洖褰掓柟娉曠殑搴旂敤涓庢瘮杈僛j]. 鏁板鐨勫疄璺典笌璁?璇? 2009, 39(10): 117-121.