Halanay inequality with Hadamard derivative and application to a neural network system

被引:6
作者
Kassim, Mohammed D. [1 ]
Tatar, Nasser-eddine [2 ]
机构
[1] Imam Abdulrahman Bin Faisal Univ, Coll Engn, Dept Basic Sci & Humanities, POB 1982, Dammam 34151, Saudi Arabia
[2] King Fahd Univ Petr & Minerals, Dept Math & Stat, Dhahran 31261, Saudi Arabia
关键词
Halanay inequality; Hadamard derivative; Mittag-Leffler stability; Hopfield neural network system; STABILITY ANALYSIS; DIFFERENTIAL-EQUATIONS; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; DYNAMICS;
D O I
10.1007/s40314-019-0874-9
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we generalize the well-known Halanay inequality from the integer-order case to the fractional case. Namely, we consider Halanay inequalities involving Hadamard fractional derivative, and discrete and distributed delays. This inequality is then applied to study the stability of neural network systems of Hopfield type. To this end, we establish some properties like the Hadamard derivative of the product of two functions and the permutation of the Hadamard derivative and the integral or the Hadamard derivative of the convolution of two functions. It is proved that the stability is of Mittag-Leffler type, in case the distributed delay kernels themselves are Mittag-Leffler decaying to zero.
引用
收藏
页数:18
相关论文
共 46 条
[11]   STABILITY OF A CLASS OF NONRECIPROCAL CELLULAR NEURAL NETWORKS [J].
CHUA, LO ;
ROSKA, T .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1990, 37 (12) :1520-1527
[12]   Storage capacity of non-monotonic neurons [J].
Crespi, B .
NEURAL NETWORKS, 1999, 12 (10) :1377-1389
[13]   New generalized Halanay inequalities with applications to stability of nonlinear non-autonomous time-delay systems [J].
Hien, L. V. ;
Phat, V. N. ;
Trinh, H. .
NONLINEAR DYNAMICS, 2015, 82 (1-2) :563-575
[14]   COMPUTING WITH NEURAL CIRCUITS - A MODEL [J].
HOPFIELD, JJ ;
TANK, DW .
SCIENCE, 1986, 233 (4764) :625-633
[15]   NEURONS WITH GRADED RESPONSE HAVE COLLECTIVE COMPUTATIONAL PROPERTIES LIKE THOSE OF 2-STATE NEURONS [J].
HOPFIELD, JJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1984, 81 (10) :3088-3092
[16]   NEURAL NETWORKS AND PHYSICAL SYSTEMS WITH EMERGENT COLLECTIVE COMPUTATIONAL ABILITIES [J].
HOPFIELD, JJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1982, 79 (08) :2554-2558
[17]   Solving interval quadratic program with box set constraints in engineering by a projection neural network [J].
Wu H. ;
He L. ;
Qin L. ;
Feng T. ;
Shi R. .
Information Technology Journal, 2010, 9 (08) :1615-1621
[18]   Retrieval phase diagrams of non-monotonic Hopfield networks [J].
Inoue, J .
JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 1996, 29 (16) :4815-4826
[19]  
Jian H., 2006, SOC PETR ENG INT EN
[20]  
Kaslik E, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P611, DOI 10.1109/IJCNN.2011.6033277