메뉴 건너뛰기




Volumn 2, Issue 2, 2011, Pages 409-418

Design and analysis of experiments in ANFIS modeling for stock price prediction

Author keywords

ANFIS; Design of experiment; Neuro fuzzy systems; Stock price prediction

Indexed keywords


EID: 79959702052     PISSN: 19232926     EISSN: 19232934     Source Type: Journal    
DOI: 10.5267/j.ijiec.2011.01.001     Document Type: Article
Times cited : (11)

References (25)
  • 1
    • 0141501530 scopus 로고    scopus 로고
    • Neuro-fuzzy methods for nonlinear system identification
    • Babuška, R., & Verbruggen, H. (2003). Neuro-fuzzy methods for nonlinear system identification. Annual reviews in control 27, 73-85.
    • (2003) Annual Reviews In Control , vol.27 , pp. 73-85
    • Babuška, R.1    Verbruggen, H.2
  • 2
    • 34548482506 scopus 로고    scopus 로고
    • A novel approach for ANFIS modelling based on full factorial design
    • Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8, 609-625.
    • (2008) Applied Soft Computing , vol.8 , pp. 609-625
    • Buragohain, M.1    Mahanta, C.2
  • 3
    • 18744430576 scopus 로고    scopus 로고
    • Comparative Study of Learning Methods in Tuning Parameters of Fuzzy Membership Functions
    • Tokyo, Japan
    • Chen, M. S. (1999). A Comparative Study of Learning Methods in Tuning Parameters of Fuzzy Membership Functions. In Proceedings of IEEE SMC '99 Conference, Tokyo, Japan, 40-44.
    • (1999) Proceedings of IEEE SMC '99 Conference , pp. 40-44
    • Chen, M.S.1
  • 4
    • 84974743850 scopus 로고
    • Fuzzy Model Identification Based on Cluster Estimation
    • Chiu, S. (1994). Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems 2(3).
    • (1994) Journal of Intelligent & Fuzzy Systems , vol.2 , Issue.3
    • Chiu, S.1
  • 5
    • 39749143324 scopus 로고    scopus 로고
    • An adaptive neuro-fuzzy system for efficient implementations
    • Echanobe, J., Campo, I. D, & Bosque, G. (2008). An adaptive neuro-fuzzy system for efficient implementations. Information Sciences, 178, 2150-2162.
    • (2008) Information Sciences , vol.178 , pp. 2150-2162
    • Echanobe, J.1    Campo, I.D.2    Bosque, G.3
  • 6
    • 0002424806 scopus 로고    scopus 로고
    • A unified parameterized formulation of reasoning in fuzzy modeling and control
    • Emami, M. R, Turksen I. B., & Goldenberg, A. A. (1999). A unified parameterized formulation of reasoning in fuzzy modeling and control. Fuzzy Sets and Systems 108, 59-81.
    • (1999) Fuzzy Sets and Systems , vol.108 , pp. 59-81
    • Emami, M.R.1    Turksen, I.B.2    Goldenberg, A.A.3
  • 8
    • 0001404416 scopus 로고
    • A new method of choosing the number of clusters for fuzzy c-means method
    • (in Japanese
    • Fukuyama, Y., & Sugeno, M. (1989). A new method of choosing the number of clusters for fuzzy c-means method. In Proceeding of 5th fuzzy system symposium (in Japanese), 247-250.
    • (1989) Proceeding of 5th Fuzzy System Symposium , pp. 247-250
    • Fukuyama, Y.1    Sugeno, M.2
  • 10
    • 0027601884 scopus 로고
    • ANFIS: Adaptive-network-based fuzzy inference systems
    • Jang, J-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man & Cybernetics, 23(3), 665-685.
    • (1993) IEEE Transactions On Systems, Man & Cybernetics , vol.23 , Issue.3 , pp. 665-685
    • Jang J-S., R.1
  • 12
    • 9544250381 scopus 로고    scopus 로고
    • A cluster validation index for GK cluster analysis based on relative degree of sharing
    • Kim, Y. I, Kim, D. W., Lee, D., & Lee, K. H. (2004). A cluster validation index for GK cluster analysis based on relative degree of sharing. Information Sciences, 168, 225-242.
    • (2004) Information Sciences , vol.168 , pp. 225-242
    • Kim, Y.I.1    Kim, D.W.2    Lee, D.3    Lee, K.H.4
  • 13
    • 0028532753 scopus 로고
    • Fuzzy systems as universal approximators
    • Kosko, B. (1994). Fuzzy systems as universal approximators. IEEE Transactions on Computers, 43(11), 1329-1333.
    • (1994) IEEE Transactions On Computers , vol.43 , Issue.11 , pp. 1329-1333
    • Kosko, B.1
  • 14
    • 0032178397 scopus 로고    scopus 로고
    • Cluster validity index for fuzzy clustering
    • Kwon, S. H. (1998). Cluster validity index for fuzzy clustering. Electronics Letters 34(22), 2176-2177.
    • (1998) Electronics Letters , vol.34 , Issue.22 , pp. 2176-2177
    • Kwon, S.H.1
  • 17
    • 14644444539 scopus 로고    scopus 로고
    • An Input-Output Clustering Approach to the Synthesis of ANFIS Networks
    • Panella, M., & Gallo, A. S. (2005). An Input-Output Clustering Approach to the Synthesis of ANFIS Networks. IEEE Transactions on Fuzzy Systems, 13(1), 69-81.
    • (2005) IEEE Transactions On Fuzzy Systems , vol.13 , Issue.1 , pp. 69-81
    • Panella, M.1    Gallo, A.S.2
  • 19
    • 53949088460 scopus 로고    scopus 로고
    • A fuzzy filter for improving the quality of the signal in adaptive-network-based fuzzy inference systems (ANFIS)
    • Riverol, C., & Di Sanctis, C. (2009). A fuzzy filter for improving the quality of the signal in adaptive-network-based fuzzy inference systems (ANFIS). Applied Soft Computing, 9(1), 305-307.
    • (2009) Applied Soft Computing , vol.9 , Issue.1 , pp. 305-307
    • Riverol, C.1    Di Sanctis, C.2
  • 20
    • 58849129653 scopus 로고    scopus 로고
    • Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods
    • Shoorehdeli, M. A., Teshnehlab, M., Sedigh, A. K, & Khanesar, M. A. (2009). Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Applied Soft Computing, 9(2), 833-850.
    • (2009) Applied Soft Computing , vol.9 , Issue.2 , pp. 833-850
    • Shoorehdeli, M.A.1    Teshnehlab, M.2    Sedigh, A.K.3    Khanesar, M.A.4
  • 22
    • 25844530263 scopus 로고    scopus 로고
    • GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
    • Tang, A. M, Quek, C., & Ng, G. S. (2005). GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms. Expert Systems with Applications 29, 769-781.
    • (2005) Expert Systems With Applications , vol.29 , pp. 769-781
    • Tang, A.M.1    Quek, C.2    Ng, G.S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.