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首页> 外文期刊>Neural Networks, IEEE Transactions on >Equivalences Between Neural-Autoregressive Time Series Models and Fuzzy Systems
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Equivalences Between Neural-Autoregressive Time Series Models and Fuzzy Systems

机译：神经自回归时间序列模型与模糊系统之间的等价关系

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摘要

Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutionary algorithms, probabilistic reasoning). Since its inception, a distinctive goal has been to dig out the deep relationships among their components. This paper considers two wide families of SC models. On the one hand, the regime-switching autoregressive paradigm is a recent development in statistical time series modeling, and it includes a set of models closely related to artificial neural networks. On the other hand, we consider fuzzy rule-based systems in the framework of time series analysis. This paper discloses original results establishing functional equivalences between models of these two classes, and hence opens the door to a productive line of research where results and techniques from one area can be applied in the other. As a consequence of the equivalences presented in this paper, we prove the asymptotic stationarity of a class of fuzzy rule-based systems. Simulations based on information criteria show the importance of the selection of the proper membership function.
机译：软计算（SC）作为许多技术的集成框架而出现，这些技术可以很好地相互补充（人工神经网络，模糊系统，进化算法，概率推理）。自成立以来，一个独特的目标就是挖掘其组成部分之间的深层关系。本文考虑了SC模型的两个大家族。一方面，状态切换自回归范式是统计时间序列建模的最新进展，它包括一组与人工神经网络密切相关的模型。另一方面，我们在时间序列分析的框架内考虑基于模糊规则的系统。本文公开了在这两种模型之间建立功能等效性的原始结果，从而为一条生产性研究打开了大门，在该领域中，可以将一个领域的结果和技术应用于另一领域。由于本文提出的等价结果，我们证明了一类基于模糊规则的系统的渐近平稳性。基于信息标准的仿真表明选择适当的隶属函数的重要性。

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