Title :
FCM in novel application of science and technology progress monitor system
Author :
Wu, Kewei ; Xie, Zhao ; Gao, Jun ; Feng, Wengang
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Hefei Univ. of Technol., Hefei
Abstract :
This paper focuses on the issues about the complex relations in large-scale FCM, and then proposes a promising method for weight global optimization with local inference to analyze and predict indexes in Anhui sci-tech progress monitor system. Firstly, a new concept, unbalanced degree, is introduced for standard evaluation in FCM model to modify the weight assessment factors and result in the satisfied convergence rate. Secondly, relations between unbalanced degree and convergence error are also presented for further analysis with training error and guarantee on perfect condition in model. Thirdly, local inference in FCM is discussed to enhance prediction accuracy rate. Finally, experimental result reveals successful application of FCM in large-scale complex sci-tech systems.
Keywords :
fuzzy set theory; inference mechanisms; large-scale systems; optimisation; Anhui sci-tech progress monitor system; fuzzy cognitive maps; large-scale FCM; large-scale complex sci-tech systems; progress monitor system; weight assessment factors; weight global optimization; Convergence; Data envelopment analysis; Educational institutions; Fuzzy sets; Fuzzy systems; Large-scale systems; Monitoring; Paper technology; Production systems; Virtual manufacturing;
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1818-3
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2008.4630355