The development of fuzzy radial basis function neural networks based on the concept of information ambiguity

Neurocomputing

Volumn 73, Issue 13-15, 2010, Pages 2464-2477

  Roh, Seok Beom ^a   Joo, Su Chong ^a   Pedrycz, Witold ^b,c,d   Oh, Sung Kwun ^e  

^a Wonkwang University   (South Korea)

^b UNIVERSITY OF ALBERTA   (Canada)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^d SYSTEMS RESEARCH INSTITUTE   (Poland)

^e Suwon University   (South Korea)

Author keywords

Ambiguity Index; Conditional fuzzy C means clustering; Radial Basis Function Neural Networks

Indexed keywords

CLUSTERING METHODS; DATA DISTRIBUTED; DESIGN ISSUES; DESIGN PROCEDURE; FUZZY C MEANS CLUSTERING; FUZZY MODELING; FUZZY MODELS; FUZZY RADIAL BASIS FUNCTION NEURAL NETWORKS; INFORMATION GRANULES; INPUT AND OUTPUTS; INPUT SPACE; INPUT-OUTPUT MAPPING; RADIAL BASIS FUNCTION NEURAL NETWORKS;

ATTITUDE CONTROL; DESIGN; FUZZY CLUSTERING; FUZZY SETS; FUZZY SYSTEMS; GRANULATION; RADIAL BASIS FUNCTION NETWORKS;

FUZZY NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CLUSTERING FUZZY SYSTEM; CONTROLLED STUDY; FUZZY SYSTEM; INFORMATION PROCESSING; LINGUISTICS; MACHINE LEARNING; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

EID: 77955332145     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.05.006     Document Type: Article

References (14)

1. Hoffmann F., Nelles O. Genetic programming for model selection of TSK-fuzzy systems. Information Sciences 2001, 136:7-28.
2. Kokshenev I., Braga A.P. A multi-objective approach to RBF network learning. Neurocomputing 2008, 71:1203-1209.
3. Lin C.J., Xu Y.J. A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications. Fuzzy Sets and Systems 2006, 157(8):1036-1056.
4. Oyang Y.J., Hwang S.C., Ou Y.Y., Chen C.Y., Chen Z.W. Data classification with radial basis function networks based on a novel kernel density estimation algorithm. IEEE Transactions on Neural Networks 2005, 16(1):225-236.
5. Pedrycz W., Multimodels Fuzzy IEEE Trans. On Fuzzy Systems 1996, 4(2):139-148.
6. Pedrycz W. Conditional Fuzzy C-means. Pattern Recognition Letters 1996, 17(6):625-632.
7. Pedrycz W. Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Networks. IEEE Transactions on Neural Networks 1998, 9(4):601-612.
8. Pedrycz W., Kwak K.C. Linguistic Models as a Framework of User-Centric System Modeling, IEEE Transactions on Systems. Man and Cybernetics- Part A 2006, 36(4):727-745.
9. Pedrycz W., Kwak K.C. The development of incremental models. IEEE Transactions on Fuzzy Systems 2007, 15(3):507-518.
10. Pedrycz W., Park H.S., Oh S.K. A granular-oriented development of functional radial basis function neural networks. Neurocomputing 2008, 72:420-435.
11. Tsekourasa G., Sarimveisb H., Kavaklia E., Bafasb G. A hierarchical fuzzy-clustering approach to fuzzy modeling. Fuzzy Sets and Systems 2005, 150(2):245-266.
12. Yang Z.R. A novel Radial Basis Function neural network for discriminant analysis. IEEE Transactions on Neural Networks 2006, 17(3):604-612.
13. Zadeh L.A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 1997, 90:111-117.
14. Park K.J., Oh S.K., Kim H.K. Optimization of fuzzy set-fuzzy systems based on IG by means of GAs with successive tuning method. Journal of Electrical Engineering and Technology 2008, 3(1):101-107.