Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach

被引:106
作者
Pu, Yi-Fei [1 ]
Zhou, Ji-Liu [1 ]
Zhang, Yi [1 ]
Zhang, Ni [2 ]
Huang, Guo [3 ]
Siarry, Patrick [4 ]
机构
[1] Sichuan Univ, Sch Comp Sci & Technol, Chengdu 610065, Peoples R China
[2] Sichuan Univ, Lib, Chengdu 610065, Peoples R China
[3] Leshan Normal Univ, Coll Comp Sci & Technol, Leshan 614000, Peoples R China
[4] Univ Paris 12, F-94010 Creteil, France
基金
中国国家自然科学基金;
关键词
Fractional calculus; fractional differential; fractional energy norm; fractional extreme point; fractional gradient; BROWNIAN-MOTION; CALCULUS; IMAGE;
D O I
10.1109/TNNLS.2013.2286175
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The application of fractional calculus to signal processing and adaptive learning is an emerging area of research. A novel fractional adaptive learning approach that utilizes fractional calculus is presented in this paper. In particular, a fractional steepest descent approach is proposed. A fractional quadratic energy norm is studied, and the stability and convergence of our proposed method are analyzed in detail. The fractional steepest descent approach is implemented numerically and its stability is analyzed experimentally.
引用
收藏
页码:653 / 662
页数:10
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