Multi-task Total Least-Squares Adaptation over Networks
Multi-task Total Least-Squares Adaptation over Networks
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DOI：
10.23919/chicc.2018.8483188
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Zhongfa Wang;L. Jia;Zi-Jiang Yang
中科院分区：
文献类型：
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作者：
Zhongfa Wang;L. Jia;Zi-Jiang Yang
协同参数估计是分布式多智能体网络的一个重要应用。在实际应用中，有许多面向多任务的应用，网络有多个最优参数向量要估计。考虑到智能体的输入和输出都被加性噪声污染，网络可以建模为多任务变量误差（M-EIV）问题。总体最小二乘（TLS）方法是解决EIV问题的一种典型方法，它可以使输入和输出数据中的扰动最小化。本文研究了节点输入被白色噪声污染的多任务网络的无偏参数估计问题。本文提出了一种新的多任务TLS（M-TLS）算法，该算法可以达到一致的无偏估计。仿真结果表明，该算法能够实现一致无偏估计。
Collaborative parameter estimation is a significant application of distributed multi-agent network. In practical scenarios, there are many multi-task oriented applications that the networks have multiple optimum parameter vectors to be estimated. Considering the condition that the input and output of agents are corrupted by additive noises, the network can be modeled as the multi-task errors-in-variables (M-EIV) problem. Total least-squares (TLS) method is a typical solution to the EIV problem for it can minimize the perturbation both in input and output data. In this paper, we study the problem of unbiased parameter estimation over multi-task networks whose nodes' inputs are corrupted by white noises. We propose a novel multi-task TLS (M-TLS) algorithm which can reach consistent unbiased estimation. Simulation results show that the proposed algorithms can achieve consistent and unbiased estimation.