MS-TSKfnn: novel Takagi-Sugeno-Kang fuzzy neural network using ART like clustering

We propose a novel architecture of neuro-fuzzy system called modified self-organizing Takagi-Sugeno-Kang fuzzy neural network (MS-TSKfnn) that uses ART-like clustering called discrete incremental clustering (DIC). The network is able to handle online data input with significant high performance. Its ability of entirely self-organizing to form the network structure without any human supervision is the main advantage over other TSK type fuzzy rule based neuro-fuzzy systems, such as ANFIS and DENFIS. Extensive simulations were conducted using MS-TSKfnn and its performance was encouraging when benchmarked against other established neural and neuro-fuzzy system.