Algorithms for deriving the rule base in Linguistic Fuzzy-Rule Based Systems usually proceed by selecting a set of candidate rules and, afterwards, finding both a subset of them and a combination of values for their consequents. Because of its cost, the latter process can be approached by using metaheuristic techniques such as genetic algorithms. However, existing works show that, if dealing with Mamdani rules – where the consequent is a linguistic label –, a basic local search clearly outperforms the genetic algorithm. In this work we aim to develop a local search algorithm to carry out the described process for the case of TSK-0 fuzzy rules, where the consequent is a real number. The experimental results show that some of the proposed algorithms clearly improve upon the state-of-the-art ones in terms of precision and number of rules, whereas learning times are fairly competitive and are orders of magnitude lower than those required by the genetic algorithm. 
Learning TSK-0 linguistic fuzzy rules by means of local search algorithms