Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Architectural Analysis of Type-2 Interval pRBF Neural Networks Using Space Search Evolutionary Algorithm

공간탐색 진화알고리즘을 이용한 Interval Type-2 pRBF 뉴럴 네트워크의 구조적 해석

  • Received : 2010.10.21
  • Accepted : 2010.12.22
  • Published : 2011.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.

본 논문에서는 RBF 뉴럴 네트워크에서 은닉층 활성함수에 Interval type-2 퍼지개념을 적용한 새로운 RBF 뉴럴 네트워크를 설계하였다. 퍼지 시스템 분야에서 불확실한 정보에 대한 Type-1 퍼지집합의 성능을 보안하고자 Type-2 퍼지집합이 제안되었으며, 멤버쉽함수 안에 다시 멤버쉽함수를 생성함으로써 불확실한 정보를 좀 더 효과적으로 다루고자 하였다. 따라서 본 논문에서는 RBF 뉴럴 네트워크의 은닉층 활성함수에 type-2 퍼지집합의 개념을 적용하여 불확실한 정보에 대한 모델 성능을 개선하고자 하였다. 나아가 연결가중치를 상수항이 아닌 1차식으로 구성된 다항식을 사용하여 최종출력을 입력-출력의 관계식으로 표현하였다. 연결가중치는 기존의 경사하강법(Gradient Descent Method; GDM) 대신 conjugate gradient method(CGM)을 사용하여 파라미터를 동조하고, 은닉층의 활성함수는 공간탐색 진화 알고리즘(Space Search Evolutionary Algorithm; SSEA)을 이용하여 가우시안 함수의 중심점 및 분포상수를 동조하여 모델의 성능을 개선시킨다. 제안된 모델의 성능을 평가하기 위해 가스로 시계열 데이터를 사용하였으며, 결과를 기존 모델과 비교하였다.

Keywords

References

  1. L. Zhao, Y. Yang, and Y. Zeng, “Eliciting Compact T-S Fuzzy Models Using Subtractive Clustering and Coevolutionary Particle Swarm Optimization,” Neurocomputing, Vol. 72, No. 10-12, pp. 2569-2575, 2009. https://doi.org/10.1016/j.neucom.2008.11.001
  2. B. Niu, Y. Zhu, X. He, and H. Shen, “A Multi-Swarm Optimizer Based Fuzzy Modeling Approach for Dynamic Systems Processing,” Neurocomputing, Vol. 71, No. 7-9, pp. 1436-1448, 2008. https://doi.org/10.1016/j.neucom.2007.05.010
  3. A. Kandal, L. Li, and Z. Cao, “Fuzzy Inference and Its Application to Control Systems,” Fuzzy Sets and Systems, Vol. 48, No. 1, pp. 99-111, 1992. https://doi.org/10.1016/0165-0114(92)90254-2
  4. Z-B. Xu, H. Q, J. Peng, and B. Zhang, “A Comparative Study of Two Modeling Approaches in Neural Network,” Neural Networks, Vol. 17, No. 1, pp. 73-85, 2004. https://doi.org/10.1016/S0893-6080(03)00192-8
  5. L. Sanchez, I. Couso, and J. Casillas, “Genetic Learning of Fuzzy Rules Based on Low Quality Data,” Fuzzy Sets and Systems, Vol. 160, No. 17, pp. 2524-2552, 2009. https://doi.org/10.1016/j.fss.2009.03.004
  6. N. N. Karnik and J. M. Mendel, “Centroid of a type-2 fuzzy set,” Information science, Vol. 132, pp. 195-220, 2001. https://doi.org/10.1016/S0020-0255(01)00069-X
  7. Y. Y. Fu, C. J. Wu, J. T. Jeng, and C. N. Ko, “Identification of MIMO systems using radial basis function networks with hybrid learning algorithm,” Applied Mathematics and Computation, Vol. 213, pp. 184-196, 2009. https://doi.org/10.1016/j.amc.2009.02.058
  8. A. Y. Al Bayati, N. A. Sulaiman, and G. W. Sadiq “A Modified Conjugate Gradient Formula for Back Propagation Neural Network Algorithm,” Journal of Computer Science, Vol. 5, No. 11, pp. 849-856, 2009. https://doi.org/10.3844/jcssp.2009.849.856
  9. W. huang, L. Ding, S. K. Oh, C. W. Jeong, and S. C. Joo, “Identification of Fuzzy Inference System Based on Information Granulation,” KSII Trans. on Internet and Information systems, Vol. 4, No. 4, pp. 575-593, 2010. https://doi.org/10.3837/tiis.2010.08.008
  10. S. K. Oh, and W. Pedrycz, “Identification of fuzzy systems by means of and auto-tuning algorithm and its application to nonlinear systems,” Fuzzy Set and Systems, Vol. 115, No. 2, pp. 205-230, 2000. https://doi.org/10.1016/S0165-0114(98)00174-2
  11. B. J. Park, W. Pedrycz and S. K. Oh, “Identification of fuzzy models with the aid of evolutionary data granulation,” In : IEE Proc. Control Theory and Applications, Vol. 148, No.5, pp. 406-418, 2001. https://doi.org/10.1049/ip-cta:20010677
  12. G. E. Tsekouras, “On the use of the weighted fuzzy C-Means in fuzzy modeling,” adv. Eng, software, Vol. 36, 287-300, 2005. https://doi.org/10.1016/j.advengsoft.2004.12.001
  13. J. N. Choi, S. K. Oh, and W. Pedrycz, “Identification of fuzzy models using a successive tuning method with a variant identification ratio,” Fuzzy Set and Systems, Vol. 159. No. 21, pp. 2873-2889, 2008. https://doi.org/10.1016/j.fss.2007.12.031