Temperature inference system by rough-neuro-fuzzy network

Rough set theory, suggested by Pawlak in 1982, has been useful in AI, machi ne learning, knowledge acquisition, knowledge discovery from databases, exp ert systems, inductive reasoning, etc. One of the main advantages of rough sets is that they do not need any preliminary or additional information abo ut data and are capable to reduce superfluous information. However, their s ignificant disadvantage in real applications is that inferences are not rea l values but disjoint intervals of real values. In order to overcome this d ifficulty, we propose a new approach in which rough set theory and neuro-fu zzy fusion are combined to obtain the optimal rule base from input/output d ata. These results are applied to the rule construction for inferring the t emperature at specified points in a refrigerator.