| Summary: | 博士 === 國立暨南國際大學 === 電機工程學系 === 101 === The discriminative capability plays a significant role in determining classification performance. To increase the discriminative capability, this thesis proposes a Takagi–Sugeno(TS)-type maximizing-discriminability-based recurrent fuzzy network (MDRFN) which can classify highly confusable patterns. 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. Moreover, to enhance the “discriminability”, an enhanced discriminability recurrent fuzzy neural network (EDRFNN) was proposed. The feedback topology of the proposed EDRFNN is fully connected in order to handle temporal pattern behavior. It is constructed from structure and parameter learning. Simulations and comparisons with other recurrent fuzzy neural networks verify the performance of proposed recurrent fuzzy neural network under noisy conditions. 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 and EDRFNN exhibit excellent classification performance.
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