Fuzzy Coefficient of Impulsive Intensity in a Nonlinear Impulsive Control System

被引：9

作者：

Onasanya, Babatunde Oluwaseun ^{[1
,2
]}

Wen, Shiping ^{[3
]}

Feng, Yuming ^{[4
,5
]}

Zhang, Wei ^{[5
]}

Xiong, Jiong ^{[5
]}

机构：

[1] Chongqing Three Gorges Univ, Key Lab Intelligent Informat Proc & Control, Chongqing 404100, Peoples R China

[2] Univ Ibadan, Dept Math, Ibadan, Nigeria

[3] Univ Technol Sydney, Australian AI Inst, Ultimo, NSW 2007, Australia

[4] Chongqing Three Gorges Univ, Chongqing Engn Res Ctr Internet Things & Intellig, Chongqing 404100, Peoples R China

[5] Chongqing Three Gorges Univ, Sch Comp Sci & Engn, Chongqing 404100, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2021年 / 53卷 / 06期

关键词：

Fuzzy coefficient of impulsive intensity; Tolerance of impulsive intensity; Impulsive control; CHAOTIC SYSTEMS; NEURAL-NETWORKS; SYNCHRONIZATION;

D O I：

10.1007/s11063-021-10614-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many works on impulsive control theory have considered some constant coefficient of impulsive intensity which, in reality, is not the case. Then, in the recent time, a model in which the coefficient of the impulsive intensity was assumed not to be certain but some J <= beta I was proposed. Unfortunately, this was also for some fixed beta. Hence, the result of this model can only give information about that particular beta. In actual sense, errors are not fixed as suggested by this recent model but vary under different circumstances. Hence, in this work, a model for infinitely many such (fuzzy) coefficients of impulsive intensity was presented and its effectiveness was demonstrated with some numerical illustrations. This idea tends to give a generalisation of the existing models. The model achieves control in less time than the aforementioned recent model.

引用

页码：4639 / 4657

页数：19

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