The modeling and prediction of time series based on synergy of high-order fuzzy cognitive map and fuzzy c-means clustering. Fuzzy cognitive maps in the modeling of granular time series. Clustering techniques for fuzzy cognitive map design for time series modeling. Interactive evolutionary optimization of fuzzy cognitive maps. Fuzzy grey cognitive networks modeling and its application. International Journal of Man-Machine Studies, 1986, 24(1): 65-75. Process time series prediction based on application of correlated process variables to CNN time delayed analyses. Data-driven prediction on performance indicators in process industry: a survey. Key words: fuzzy time cognitive maps, prediction, cross-correlation function, particle swarm optimization, time-delay system The TM-FTCM method has been verified by numerical simulations and actual chemical plant process data to be efficient and practical. Furthermore, the optimization of self-impact factors, bias and transfer functions enhances the efficiency of the prediction process. The cross-correlation functions (CCF) helps to find the time-delay factors hiding in the big data, thus revealing the actual structure of the model. ![]() ![]() The time-delay-mining fuzzy time cognitive maps (TM-FTCM) method is proposed to enhance the accuracy of the time-delay prediction model. Therefore, the prediction result is inconvincible and unpredictable. The causal relationship between variables can t be calculated accurately. However, time-delay among industrial process variables is always ignored in traditional FCM models. Fuzzy cognitive maps (FCM), as a modeling tool for complex systems, can handle the nonlinearity and uncertainty of the system.
0 Comments
Leave a Reply. |