Loop-shaped Distributed Learning of an Object with Data-independent Performance Certificates
Loop-shaped Distributed Learning of an Object with Data-independent Performance Certificates
复制标题
具有数据独立性能证书的对象的循环分布式学习
DOI：
10.1080/01691864.2022.2128872
复制
发表时间：
2022
影响因子：
2
通讯作者：
Takeshi Hatanaka
中科院分区：
文献类型：
--
作者：
Toshiyuki Oshima;Shunya Yamashita;Junya Yamauchi;Tatsuya Ibuki;Michio Seto;Takeshi Hatanaka
本文讨论了使用多个机器人的对象形状的分布式学习，并提出了一个系统的设计过程与数据无关的性能证书的分布式优化算法。我们首先将对象形状学习制定为基于所谓的核方法的分布式分类问题。一个分布式算法，连续时间交替方向的乘法器，然后施加到问题，其中观察到的瞬态性能差。为了提高性能，我们重新制定的分类问题，使算法中的子块的奇异值进行适当的缩放。然后，我们提出了一个系统的设计过程中的算法的基础上的概念循环成形。对该方法进行了进一步的扩展，使其性能与数据无关，并通过数值算例验证了其有效性。所提出的方法，最后证明了通过仿真的高保真仿真器。
This paper addresses distributed learning of object shapes using multiple robots, and proposes a systematic design procedure for distributed optimization algorithms with data-independent performance certificates. We start with formulating the object shape learning as a distributed classification problem based on so-called kernel method. A distributed algorithm, continuous-time alternating direction method of multipliers, is then applied to the problem, wherein poor transient performances are observed. To improve the performance, we reformulate the classification problem so that singular values of sub-blocks in the algorithm are appropriately scaled. We then propose a systematic design procedure of the algorithm based on the concept of loop-shaping. The procedure is further extended so that the performance is independent of the data, and its effectiveness is verified through a numerical example. The proposed method is finally demonstrated through simulation on a high fidelity simulator.