Title :
Research of Chemical Modeling Method Based on Rough-Fuzzy Inference System
Author_Institution :
Sch. of Math. & Comput., Ningxia Univ., Yinchuan, China
Abstract :
Based on the rule derived from the Rough Sets methodology, we designed the fuzzy neural networks. Using the regular parameter and the default value that are estimated by the discretization results, the network is up to an optimal value rapidly by large amount of training. When applied to model the solvent dehydrating tower in the PTA complex process, the performance is superior to the common feed-forward neural network. The fuzzy neural network can eliminate redundant information of the decision system and reduce modeling complexity. In the practical application, the system represents its dominance at quickly convergence and powerful generalization.
Keywords :
chemical engineering computing; chemistry computing; feedforward neural nets; fuzzy neural nets; inference mechanisms; rough set theory; chemical modeling method; decision system; feedforward neural network; fuzzy neural networks; modeling complexity; redundant information; rough fuzzy inference system; rough sets methodology; Chemicals; Design methodology; Feedforward neural networks; Feedforward systems; Fuzzy neural networks; Neural networks; Poles and towers; Power system modeling; Rough sets; Solvents;
Conference_Titel :
e-Business and Information System Security (EBISS), 2010 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5893-6
Electronic_ISBN :
978-1-4244-5895-0
DOI :
10.1109/EBISS.2010.5473716
