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

页码：436 / 443

页数：8

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