FCM in novel application of science and technology progress monitor system

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.