Continuous-Time Multi-agent Network for Distributed Least Absolute Deviation

被引：2
|
作者：
Liu, Qingshan [1 ]
Zhao, Yan [2 ]
Cheng, Long [3 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[2] Wannan Med Coll, Dept Basic Courses, Wuhu 241000, Anhui, Peoples R China
[3] Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2015 | 2015年 / 9377卷
关键词：
Distributed least absolute deviation; multi-agent network; nonsmooth optimization; consensus; PROJECTION NEURAL-NETWORK; VARIATIONAL-INEQUALITIES; OPTIMIZATION PROBLEMS; NONLINEAR DYNAMICS; CONSENSUS; SYSTEMS;
D O I：
10.1007/978-3-319-25393-0_48
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a continuous-time multi-agent network for distributed least absolute deviation (DLAD). The objective function of the DLAD problem is a sum of many least absolute deviation functions. In the multi-agent network, each agent connects with its neighbors locally and they cooperate to obtain the optimal solutions with consensus. The proposed multi-agent network is in fact a collective system with each agent being considered as a recurrent neural network. Simulation results on a numerical example are presented to illustrate the effectiveness and characteristics of the proposed distributed optimization method.
引用
收藏
页码：436 / 443
页数：8
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