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


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.


Supported by : National Research Foundation of Korea (NRF)


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