A TS-Type Maximizing-Discriminability-Based Recurrent Fuzzy Network for Classification Problems

This work proposes a Takagi-Sugeno (TS)-type maximizing-discriminability-based recurrent fuzzy network (MDRFN) that can classify highly confusable patterns. The discriminative capability plays a significant role in determining classification performance. To increase the discriminative capability, the proposed MDRFN considers minimum classification error (MCE) and minimum training error (MTE). In MCE, the weights are updated by maximizing the discrimination among different classes. In MTE, the parameter learning adopts the gradient-descent method to reduce the cost function. Therefore, the novelty of MDRFN is that it not only minimizes the cost function but maximizes the discriminative capability as well. The effectiveness of the proposed MDRFN is demonstrated by three temporal classification problems. In the experiments, other RFNs, including the singleton-type recurrent neural fuzzy network (SRNFN), TS-type RFN (TRFN), and simple RFN (SRFN), are compared. Analysis results indicate that the proposed MDRFN exhibits excellent classification performance.