Learning Methods for Fuzzy Inference System Using Vector Quantization

Fast Learning Algorithm for Fuzzy Inference Systems using Vector Quantization

It is known that learning methods of fuzzy inference systems using vector quantization (VQ) and steepest descend method (SDM) are superior in terms of the number of rules. However, they need a great deal of learning time. The cause could be that both of VQ and SDM perform only local searches. On the other hand, it has been shown that a learning method of radial basis function (RBF) networks usi...

Fuzzy-Kernel Learning Vector Quantization

This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the wellknown fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results...

A methodology for constructing fuzzy algorithms for learning vector quantization

This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ alg...

Learning-Theoretic Methods in Vector Quantization

Automatic Speaker Recognition Using Fuzzy Vector Quantization

Speaker recognition (SR) is a dynamic biometric task. SR is a multidisplinary problem that encompasses many aspects of human speech, including speech recognition, language recognition, and speech accents. This technique makes it possible to use the speaker’s voice to verify his/her identity and provide controlled access to services. The Mel-frequency extraction method is leading approach for sp...