Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Fused Fuzzy Logic System for Corrupted Time Series Data Analysis

훼손된 시계열 데이터 분석을 위한 퍼지 시스템 융합 연구

  • Kim, Dong Won (Dept. of Digital Electronics, Inha Technical College)
  • 김동원 (인하공업전문대학 디지털전자과)
  • Received : 2018.01.25
  • Accepted : 2018.03.10
  • Published : 2018.06.30
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Abstract

This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

본 논문에서는 노이즈에 의해 훼손된 시계열 데이터의 모델링에 대하여 다룬다. 모델링 기법으로, 논싱글톤 퍼지 시스템을 사용한다. 논싱글톤 퍼지 시스템의 주요특징은 미지의 비선형시스템의 입력이 퍼지값으로 모델링 된다는데 있다. 그러므로 퍼지시스템에 인가되는 학습데이터나 입력데이터 등이 노이즈나 외부 환경에 의해 변형된 경우에 매우 유용하게 적용될 수 있다. 성능비교를 위해 벤치마크 데이터로 잘 알려진 Mackey-Glass 데이터를 사용한다. 이들 데이터 모델링을 통하여 결과를 비교, 분석하여 논싱글톤 퍼지시스템이 잡음에 대하여 보다 강인하고 효율적임을 본 논문에서 보인다.

Keywords

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