In this paper, an approach to autonomous learning of a multimodel system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types of local models forming multimodel systems. It is fully data driven and its structure is decided by the nonparametric data clouds extracted from the empirically observed data without making any prior assumptions concerning data distribution and other data properties. All metaparameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory and calculation efficiencies of the proposed algorithm. The structural evolution mechanism and online data cloud quality monitoring mechanism of the ALMMo system largely enhance the ability of handling shifts and/or drifts in the streaming data pattern. Numerical examples of the use of ALMMo system for streaming data analytics, classification, and prediction are presented as a proof of the proposed concept.

中文翻译：

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从数据流自主学习多模型系统


本文提出了一种从流数据中自主学习多模型系统的方法，称为 ALMMo。所提出的方法是通用的，并且可以很容易地应用于形成多模型系统的概率模型或其他类型的局部模型。它完全是数据驱动的，其结构由从经验观察的数据中提取的非参数数据云决定，而不对数据分布和其他数据属性做出任何事先假设。该系统的所有元参数都是直接从数据中获得的，并且可以递归更新，这提高了该算法的存储和计算效率。 ALMMo系统的结构演化机制和在线数据云质量监控机制极大地增强了处理流数据模式中的移位和/或漂移的能力。使用 ALMMo 系统进行流数据分析、分类和预测的数值示例作为所提出概念的证明。