artificial neural network (ANN) of the radial basis function type. ANN learning algorithms can
be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling
approach has preference to explain solutions over completely black-box models, such as ANN.
In this paper, we implement the design of experiment (DOE) technique to identify the
significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS) for
stock price prediction.
How to cite this paper
Gharakhani, M., Alizadeh, M., Fotoohi, E & Rada, R. (2011). Design and analysis of experiments in ANFIS modeling for stock price prediction.International Journal of Industrial Engineering Computations , 2(2), 409-418.
Refrences
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Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8, 609–625.
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.
Chiu, S. (1994). Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems 2(3).
Echanobe, J., Campo, I. D, & Bosque, G. (2008). An adaptive neuro-fuzzy system for efficient implementations. Information Sciences, 178, 2150–2162.
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.
Fazel Zarandi, M. H, Rezaee, B., Turksen, I. B., & Neshat, E. (2009). A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Systems with Applications 36, 139–154.
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.
Hicks, C. R. (1999). Fundamental Concepts in the Design of Experiments. Oxford University Press: New York.
Jang, J-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man & Cybernetics, 23(3), 665–685.
Jang, J-S. R, & Mizutani, E. (1996). Levenberg-Marquardt Method for ANFIS Learning. In Proceedings of Biennial Conference of the North American Fuzzy Information Processing Society, 87 -91.
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.
Kosko, B. (1994). Fuzzy systems as universal approximators. IEEE Transactions on Computers, 43(11), 1329-1333.
Kwon, S. H. (1998). Cluster validity index for fuzzy clustering. Electronics Letters 34(22), 2176–2177.
Mascioli, F.M., Varazi, G.M, & Martinelli, G. (1997). Constructive Algorithm for Neuro-Fuzzy Networks. In Proceedings of the Sixth IEEE International Conference Fuzzy Systems, Spain, 1, 459- 464.
Melek, W. W., Goldenberg, A. A., & Emami, M. R. (2005). A fuzzy noise-rejection data partitioning algorithm. International journal of approximate reasoning 38, 1-17.
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.
Pedrycz, W., & Reformat, M. (2003). Evolutionary Fuzzy Modeling. IEEE Transactions on Fuzzy Systems 11(5), 652-665.
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.
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.
Sugeno, M., & Yasukawa, T. (1993). A fuzzy logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1, 7–31.
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.
Teshnehlab, M., Aliyari Shoorehdeli, M., & Sedigh, A.K. (2008). Novel hybrid learning for tuning ANFIS parameters as an identifiers using fuzzy PSO. In Proceedings of IEEE International Conference on Networking, Sensing and Control, Sanya, China, 111-116.
Tsoukalas, L. H., & Uhrig, R. E. (1997). Fuzzy and Neural Approaches in Engineering. John Wiley & Sons: New York.
Zanchettin, C., Minku, F. L, & Ludermir, T. B. (2005). Design of Experiments in Neuro-Fuzzy Systems. In Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (HIS’05), 6, doi: 10.1109/ICHIS.2005.34.
Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8, 609–625.
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.
Chiu, S. (1994). Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems 2(3).
Echanobe, J., Campo, I. D, & Bosque, G. (2008). An adaptive neuro-fuzzy system for efficient implementations. Information Sciences, 178, 2150–2162.
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.
Fazel Zarandi, M. H, Rezaee, B., Turksen, I. B., & Neshat, E. (2009). A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Systems with Applications 36, 139–154.
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.
Hicks, C. R. (1999). Fundamental Concepts in the Design of Experiments. Oxford University Press: New York.
Jang, J-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man & Cybernetics, 23(3), 665–685.
Jang, J-S. R, & Mizutani, E. (1996). Levenberg-Marquardt Method for ANFIS Learning. In Proceedings of Biennial Conference of the North American Fuzzy Information Processing Society, 87 -91.
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.
Kosko, B. (1994). Fuzzy systems as universal approximators. IEEE Transactions on Computers, 43(11), 1329-1333.
Kwon, S. H. (1998). Cluster validity index for fuzzy clustering. Electronics Letters 34(22), 2176–2177.
Mascioli, F.M., Varazi, G.M, & Martinelli, G. (1997). Constructive Algorithm for Neuro-Fuzzy Networks. In Proceedings of the Sixth IEEE International Conference Fuzzy Systems, Spain, 1, 459- 464.
Melek, W. W., Goldenberg, A. A., & Emami, M. R. (2005). A fuzzy noise-rejection data partitioning algorithm. International journal of approximate reasoning 38, 1-17.
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.
Pedrycz, W., & Reformat, M. (2003). Evolutionary Fuzzy Modeling. IEEE Transactions on Fuzzy Systems 11(5), 652-665.
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.
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.
Sugeno, M., & Yasukawa, T. (1993). A fuzzy logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1, 7–31.
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.
Teshnehlab, M., Aliyari Shoorehdeli, M., & Sedigh, A.K. (2008). Novel hybrid learning for tuning ANFIS parameters as an identifiers using fuzzy PSO. In Proceedings of IEEE International Conference on Networking, Sensing and Control, Sanya, China, 111-116.
Tsoukalas, L. H., & Uhrig, R. E. (1997). Fuzzy and Neural Approaches in Engineering. John Wiley & Sons: New York.
Zanchettin, C., Minku, F. L, & Ludermir, T. B. (2005). Design of Experiments in Neuro-Fuzzy Systems. In Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (HIS’05), 6, doi: 10.1109/ICHIS.2005.34.