Crystal growth furnace is the platform of material melting experiment in space station. The precision of the temperature controller of the furnace determines whether the experiment is successful or not. Generally, system identification contributes to the design of a better controller. However, many identification methods are only effective with large datasets and cannot track the changing features of the system characteristics when the environment changes. In this article, we present an online method to identify the time-varying and large time delay furnace system. Inspired by clustering theory, to track the latest characteristics of the system, the training set is dynamically updated based on sample similarities. Grid search and grey wolf optimizer are used respectively for hyperparameter optimization in a two-phase tuning process. The presented identification method is validated using Tiangong-2 furnace data set. The results show the established recursive least-squares SVMs can successfully predict the temperatures of the furnace with different experiment environment.
In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in [Formula: see text] space) of SVM are arbitrarily distributed among [Formula: see text] nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we prove a lower bound on the communication complexity and give a distributed [Formula: see text]-approximation algorithm to reach this lower bound, where [Formula: see text] is a user specified small constant. For distributed SVM with outliers, we present a [Formula: see text]-approximation algorithm to explicitly remove the influence of outliers. Our algorithm is based on a deterministic distributed top [Formula: see text] selection algorithm with communication complexity of [Formula: see text] in the coordinator model.
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