Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization (MSSVQ), which is a hybrid of Multi, switched, split vector quantization techniques. The spectral distortion performance, computational complexity, and memory requirements of MSSVQ are compared to spl...

Antipollution legislation in automotive internal combustion engines requires active control of pollutant formation and emissions. In addition to new technologies, like selective catalyst systems or diesel particulate filters, predictive emission models are needed. These models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagno...

An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen 's self-organizing feature map, learning vector quantization and back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of c...

OBJECTIVE A self-organizing map (SOM) is a competitive artificial neural network with unsupervised learning. To increase the SOM learning effect, a fuzzy-soft learning vector quantization (FSLVQ) algorithm has been proposed in the literature, using fuzzy functions to approximate lateral neural interaction of the SOM. However, the computational performance of FSLVQ is still not good enough, espe...

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...

A construction of the 2d and 4d fuzzy de Sitter hyperboloids is carried out by using a (vector) coherent state quantization. We get a natural discretization of the dS “time” axis based on the spectrum of Casimir operators of the respective maximal compact subgroups SO(2) and SO(4) of the de Sitter groups SO0(1, 2) and SO0(1, 4). The continuous limit at infinite spins is examined.

In this paper, an algorithm for datadriven incremental learning of fuzzy basis function networks is presented. A modified version of vector quantization is exploited for rule evolution and incremental learning of the rules’ antecedent parts. Antecedent learning is connected in a stable manner with a recursive learning of rule consequent functions with linear parameters. The paper is concluded w...