[ WANG L X, M ENDEL JM. Fuzzy basis functions, un ive rsal approx im ation, and orthogonal least squares lea rning[ J]. IEEE T rans on Neural Netwo rks, 1992, 3( 5): 807-814.
[3]
[ WANG L X, MENDEL JM. G enerating fuzzy rules by learn ing from ex am ples[ J]. IEEE T rans on Sy stem sM an and Cybern, 1992, 22( 6): 1414-1427.
[4]
[ ABE S, LAN M S. Fuzzy rules ex traction directly from num erical data for function approx im ation[ J]. IEEE Trans Sy stM an Cybern, 1995, 25: 119-129.
[5]
[ TAKAGI T, SUGENO M. Fuzzy identification o f system s and its applica tions to mode ling and contro l[ J]. IEEE Trans Syst M an Cybern, 1985, 15( 1): 116-132.
[6]
[ ROJAS I, POMARES H, ORTEGA J, e t a.l Sel-f o rganized fuzzy sy stem generation from tra in ing ex am ples[ J]. IEEE T rans Fuzzy Syst, 2000, 8: 23-36.
[7]
[ POMARES H, ROJAS I, ORTEGA J, et a.l A system a tic approach to a se l-f gene rating fuzzy rule-table for function approx im ation [ J]. IEEE T rans Sy stM an Cybe rn, 2000, 30: 431-447.
[8]
[ HONG T, LEE C. Induction of fuzzy rules and membership functions from tra in ing ex am ples[ J]. Fuzzy Se ts Sy st, 1996, 84: 33-37.
[9]
GAO X inbo. Fuzzy C luster Ana ly sis and Its Applitions[M ]. X i’ an: Xidian University Press, 2004. ( in Ch inese)
[10]
[ KENNEDY J, EBERHART R. Particle sw arm optim za tion[ C] / / Proceed ings of the IEEE Inte rnational Conference on Neural N etworks. 1995: 1942-1948.
[11]
WANG L ix in. Fuzzy system: opertun ity coex iting w ith challenge―― d igestion of ten-year research[ J]. A ctaAutom atica Sin-i ca, 2001, 27( 4): 585-590. ( in Chinese)
[12]
[ SH IGEO ABE, LAN M INGSHONG. Fuzzy ru les ex traction direc tly fo rm num er ica l data for function approx im a tion[ J]. IEEE T rans Sy stM an Cybe rn, 2000, 25( 1) : 119-129.
[13]
[ WU TZUPING, CHEN SHY IM ING. A new m e thod for constructingm enbership functions and fuzzy rules from tra in ing ex amp les[ J]. IEEE Trans SystM an Cybern, Pa rt B: Cybe rnetics, 1999, 29( 1) : 25-40.
[14]
[ ROJAS I, POMARES H, FEMANDEZ F J, et a.l A new m ethodology to obta in fuzzy sy stem s autonom ously from tra in ing data [ C ] / / IEEE Internationa l Fuzzy System s Confe rence Proceed ings FUZZ-IEEE’ 99. Seou:l [ s. n. ] , 1999, 1: 527-532.
[15]
[ HECTOR POMARES, IGNACIO RO JAS, JESU’S GONZ IEZ, et a.l Structure identifica tion in com plete ru le-based fuzzy system s[ J]. IEEE T ransactions on Fuzzy System s, 2002, 10( 3): 349-359.
[16]
[ JANETTE C. CANO, PATR IC IA A. Nava, a fuzzy me thod for automa tic generation of m embersh ip function using fuzzy relations from training exam ples [ C] / /Annua lM eeting of theNo rth Am er ican Fuzzy Inform a tion Processing So ciety ( NAFI PSFLINT 2002). New Orleans, 2002: 158-162.
LIH ongx ing. Se l-f adaptive fuzzy contro ller w ith chang ing un iverse o f d iscourse [ J]. Science in China ( Ser ies E) , 1999, 29( 1): 32-42. ( in Ch inese)
[19]
[ SUDKAM P T, HAMMELL R J. Interpo lation, comp le tion and learn ing fuzzy rules[ J]. IEEE Trans SystM an Cybe rn, 1994, 24: 332-342.
[20]
[ 边肇祺. 模式识别[M ]. 北京: 清华大学出版社, 2000: 234-241.
[21]
B IAN Zhaoq.i Pa ttern Recogn ition[M ]. Be ijing: Ts inghua University Press, 2000: 234-241. ( in Chinese)
[22]
[ 高新波. 模糊聚类分析及其应用[M ]. 西安: 西安电子科技大学出版社, 2004.
[23]
[ EBERHART R, KENNEDY J. A new optim ze r using partic le sw arm theory [ C] / / Proceedings o f the 6th In ternational Symposium onM icroM achine and H umm an Sc ience Pisca taw ay, Nagoy a: IEEE Serv ice Con ter, 1995: 39-43.
LIU Jingm ing, HAN Lichuang, HOU L iw en. A new cluster algorithm―― partic le swarm c luster a lgo rithn[ J]. Com puter Eng ineer ing and Applications, 2005( 20): 183-185. ( in Ch inese)
