An optimal design of interval type-2 fuzzy logic system with various experiments including magnetorheological fluid damper

This paper focuses on optimal design of an interval type-2 fuzzy logic system (IT-2FLS) to cope with uncertainty issue of training set and noisy data. Content of the solution is depicted based on the proposed algorithm to optimally design an IT-2FLS from a dataset, named OD-T2FLS. The major concept of the OD-T2FLS is a combination of a useful method of clustering data space to establish a type-1 fuzzy logic system (T-1FLS) and an appropriate way to transform the T-1FLS into an IT-2FLS as well as to optimally adjust parameters of the IT-2FLS. Firstly, an improved algorithm to establish an adaptive neuro-fuzzy inference system (ANFIS), named IM-ENFS, is presented. Based on the given dataset, clustering in the join input–output data space is realized to establish a cluster-data space. Using the IM-ENFS for this cluster-data space, together with the cluster-data space optimized, an ANFIS having a role as an optimal T1-FLS is also established. Parameters of the optimized T-1FLS are then used to build the initial structure of IT-2FLS. Subsequently, this IT-2FLS is optimally adjusted based on the well-known genetic algorithm. Finally, to demonstrate the effectiveness of the proposed OD-T2FLS, experiments including magnetorheological fluid damper are realized based on two different statuses of data sources, with and without noise.