사물인터넷융복합논문지 (Journal of Internet of Things and Convergence)

한국사물인터넷학회 (The Korea Internet of Things Society)
권호 표지
DOI QR코드

DOI QR Code

훼손된 시계열 데이터 분석을 위한 퍼지 시스템 융합 연구

Fused Fuzzy Logic System for Corrupted Time Series Data Analysis

  • 김동원 (인하공업전문대학 디지털전자과)
  • Kim, Dong Won (Dept. of Digital Electronics, Inha Technical College)
  • 투고 : 2018.01.25
  • 심사 : 2018.03.10
  • 발행 : 2018.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

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.

키워드

참고문헌

  1. M.C. Mackey, and L. Glass, "Oscillation and chaos in physiological control systems," Sci., vol. 197, pp.287-289, 1977. https://doi.org/10.1126/science.267326
  2. D. Kim, S.H, Huh, and G.T. Park, "Modeling Corrupted Time Seires Data via Nonsingleton Fuzzy Logic System," Lect. Notes Comput. Sc, vol, 3316, pp.1298-1303, 2004.
  3. G.C. Mouzouris and J.M. Mendel, "Nonsingleton Fuzzy Logic Systems: Theory and Application," IEEE Trans. Fuzzy Syst. vol. 5, no. 1, pp.56-71, 1997. https://doi.org/10.1109/91.554447
  4. M. Balazinski, E. Czogala, and T. Sadowski, "Control of metal-cutting process using neural fuzzy controller," in 2nd IEEE Int. Conf. Fuzzy Syst., San Francisco, CA, Mar. vol. 1, pp.161-166, 1993.
  5. Mendel J, "Type-2 fuzzy sets and systems: An overview". IEEE Comput. Intell. Mag. 3(1): pp.20-29 2007.
  6. Mendel J, John R, Liu F. "Interval type-2 fuzzy logic systems made simple". IEEE Trans. Fuzzy Syst. 14(6): pp.808-821. 2006. https://doi.org/10.1109/TFUZZ.2006.879986
  7. Castillo O, Melin P. "A review on the design and optimization of interval type-2 fuzzy controllers". Appl. Soft Comput. 12(4): pp.1267-1278, 2012. https://doi.org/10.1016/j.asoc.2011.12.010
  8. Melin P, Mendoza O, Castillo O, "Face Recognition With an Improved Interval Type-2 Fuzzy Logic Sugeno Integral and Modular Neural Networks". IEEE Transactions on Systems, Man, and Cybernetics, Part A 41(5): pp.1001-1012, 2011. https://doi.org/10.1109/TSMCA.2010.2104318
  9. Castillo O, Melin P, Pedrycz W. "Design of interval type-2 fuzzy models through optimal granularity allocation". Appl. Soft Comput. 11(8): pp.5590-5601, 2011. https://doi.org/10.1016/j.asoc.2011.04.005
  10. Melin P, Mendoza O, Castillo O. "An improved method for edge detection based on interval type-2 fuzzy logic". Expert Syst. Appl. 37(12): pp.8527-8535, 2010. https://doi.org/10.1016/j.eswa.2010.05.023
  11. Tan W W and Wu D. "Design of type-reduction strategies for type-2 fuzzy logic systems using genetic algorithms". Stud. Comput. Intell. 66: pp.169-187, 2007.
  12. Liu F. "An efficient centroid type-reduction strategy for general type-2 fuzzy logic system". Inf. Sci. 178(9): pp.2224-2236, 2008. https://doi.org/10.1016/j.ins.2007.11.014
  13. Hameed I, "Simplified architecture of a type-2 fuzzy controller using four embedded type-1 fuzzy controllers and its application to a greenhouse climate control system", Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng. 223(5), pp.619-631. 2009.
  14. Hidalgo D, Melin P, Castillo O, "An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms", Expert Syst. Appl. 39(4), pp.4590-4598, 2012. https://doi.org/10.1016/j.eswa.2011.10.003
  15. Kim Y, An J, Kim J, "A fuzzy obstacle avoidance controller using a lookup-table sharing method and its applications for mobile robots", Int. J. Adv. Robot. Syst. 8(5), pp.39-48, 2011. https://doi.org/10.5772/45704