From local neural networks to granular neural networks: A study in information granulation

Neurocomputing

Volumn 74, Issue 18, 2011, Pages 3931-3940

  Song, Mingli ^a   Pedrycz, Witold ^a,b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Granular neural networks; Information granularity; Information granules; Intervals

Indexed keywords

DATA SETS; INFORMATION GRANULARITY; INFORMATION GRANULATION; INFORMATION GRANULES; INTERVALS; MACHINE-LEARNING; PERFORMANCE INDICES; QUALITY OF INFORMATION;

GRANULATION;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; GRANULAR NEURAL NETWORK; INFORMATION GRANULATION; INFORMATION PROCESSING; LOCAL NEURAL NETWORK; PRIORITY JOURNAL; PROCESS DESIGN; QUALITY CONTROL; QUANTITATIVE ANALYSIS;

EID: 80053314740     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.08.009     Document Type: Article

References (22)

1. Pedrycz W., Vukovich G. Granular neural networks. Neurocomputing 2001, 36:205-224.
2. Park H., Pedrycz W., Oh S. Granular neural networks and their development through context based clustering and adjustable dimensionality of receptive fields. IEEE Trans. Neural Networks 2009, 20:1604-1616.
3. Zhang Y.Q., Jin B., Tang Y. Granular neural networks with evolutionary interval learning. IEEE Trans. Fuzzy Syst. 2008, 16:309-319.
4. Zhang Y.Q., Fraser M.D., Gagliano R.A., Kandel A. Granular neural networks for numerical-linguistic data fusion and knowledge discovery. IEEE Trans. Neural Networks 2000, 11:658-667.
5. Pedrycz W., Park H.S., Oh S.K. A granular-oriented development of functional radial basis function neural networks. Neurocomputing 2008, 72:420-435.
6. Panoutsos G., Mahfouf M. A neural-fuzzy modeling framework based on granular computing: concepts and applications. Fuzzy Sets Syst. 2010, 161:2808-2830.
7. Pedrycz W. Logic-based fuzzy neurocomputing with unineurons. IEEE Trans. Fuzzy Syst. 2006, 14:860-873.
8. Nabavi-Kerizi S.H., Abadi M., Kabir E., PSO-based A weighting method for linear combination of neural networks. Comput. Electr. Eng. 2010, 36:886-894.
9. Akhand M.A.H., MonirulIslam Md., Murase K. Progressive interactive training: a sequential neural network ensemble learning method. Neurocomputing 2009, 73:260-273.
10. Das Resul, Turkoglu Ibrahim, Sengur Abdulkadir Effective diagnosis of heart disease through neural networks ensembles. Expert Syst. Appl. 2009, 36:7675-7680.
11. Green Michael, Ekelund Ulf, Edenbrandt Lars, Björk Jonas, Forberg Jakob Lundager, Ohlsson Mattias Exploring new possibilities for case-based explanation of artificial neural network ensembles. Neural Networks 2009, 22:75-81.
12. Kim Kyung-Joong, Cho Sung-Bae Evolutionary ensemble of diverse artificial neural networks. Neurocomputing 2008, 71:1604-1618.
13. Granitto P.M., Verdes P.F., Ceccatto H.A. Neural network ensembles: evaluation of aggregation algorithms. Artif. Intell. 2005, 163:139-162.
14. Zadeh L.A. Fuzzy sets and information granularity. Advances in Fuzzy Set Theory and Applications 1979, 3-18. Amsterdam, North Holland. M.M. Gupta, R.K. Ragade, R.R. Yager (Eds.).
15. Zadeh L.A. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst. 1983, 11:199-227.
16. Pedrycz W., Aliev R.A. Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 2009, 73:10-23.
17. Du D., Li K., Fei M. A fast multi-output RBF neural network construction method. Neurocomputing 2010, 73:2196-2202.
18. A V.D. Searching for a solution to the automatic RBF network design problem. Neurocomputing 2002, 42:147-170.
19. McCulloch W., Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 1943, 5:115-133.
20. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, Wiley, New York.
21. Schapire R. Strength of weak learnability. Mach. Learning 1990, 5:197-222.
22. Freund Y., Schapire R.E., Decision-Theoretic A Generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997, 55:119-139.