摘要：This research paper describes the fuzzy logic and hybrid fuzzy logic-based systems, which are utilized to create knowledge-based frameworks in chemical engineering. In this study, we have proposed fuzzy logic-based methods such as subtractive clustering (SC) and integrated approach of SC with artificial neural network fuzzy inference system (SC-ANFIS) for calculating the rate of chemical reactions. The root mean square error (in both training and testing data) of SC-ANIFIS is less as compared to existing FCM method. So, the proposed methods give better results as compared to the exiting methods such as classical fuzzy logic and fuzzy C-means (FCM).
其他摘要：This research paper has been described the fuzzy logic and hybrid fuzzy logic-based systems, which is utilize to create knowledge-based frameworks in chemical engineering. In this proposed study, we have proposed fuzzy logic-based methods such as Fuzzy sets, Fuzzy C-Means (FCM), Subtractive Clustering (SC) and integrated approach of SC and Artificial Neural Network Fuzzy Inference System (SC-ANFIS) for calculating the rate of chemical reaction. After comparing the results of these proposed methods with other exiting methods such as classical fuzzy logic, FCM and subtractive clustering (SC) methods, gives get the better result of SC-ANFIS. The root mean square error (both training and testing data) of SC-ANIFIS is less as compared to existing FCM method.
