Fuzzy Inference Considering Data Aging in Smart Rules Engine

2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T) Pub Date : 2020-10-06 DOI:10.1109/PICST51311.2020.9468100

A. Kargin, T. Petrenko

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Abstract

When developing the Internet of Things and Smart Systems, framework based on pattern technology are used. The Rules Engine (RE) pattern implements the functionality of the system under development. A new model Smart RE (SRE) with a two-stage data processing is discussed. The Abstracting Engine (AE) on a first processing stage, are integrated with RE which implements traditional Fuzzy Logic System (FLS) on second stage. The AE maps the data from sensors into facts (words) that reveal the meaning of the data. The fact model takes into account various types of uncertainty, including those associated with data aging over time. The Certainty Factor (CF) as a numerical characteristic of a fact is an input variable of a fuzzy inference mechanism at the second stage of data processing in RE. This article discusses the requirements for the knowledge representation (rules and facts) and the features of the RE operating (fuzzy logic inference), allow using the quantitative estimates of data aging effect in FLS. Three FLS encapsulation options as RE in SRE on an example of application of a Smart Traffic Light system are considered.

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智能规则引擎中考虑数据老化的模糊推理

在开发物联网和智能系统时，采用了基于模式技术的框架。规则引擎(RE)模式实现正在开发的系统的功能。讨论了一种两阶段数据处理的智能RE (SRE)模型。在第一处理阶段采用抽象引擎(AE)，第二处理阶段采用正则化(RE)实现传统模糊逻辑系统(FLS)。AE将来自传感器的数据映射为揭示数据含义的事实(单词)。事实模型考虑了各种类型的不确定性，包括与数据随时间老化相关的不确定性。确定性因子(CF)作为事实的数值特征，是可重构数据处理第二阶段模糊推理机制的输入变量。本文讨论了可重构对知识表示(规则和事实)的要求和可重构操作(模糊逻辑推理)的特点，允许在可重构数据处理中使用数据老化效应的定量估计。以智能交通灯系统的应用为例，考虑了三种FLS封装方案，如SRE中的RE。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T)

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