Genetic algorithms have been widely used to solve optimization in many fields such as multi-objective optimization, Fuzzy Optimization, and scheduling problem. Vector quantization, a basic method, is adopted by image compression technology and has a better performance than scalar quantization. Hence, it is worth to study how to apply genetic algorithms on the optimal design of codebook generati...

This article presents a fuzzy self-adaptive particle swarm optimization (FSAPSO) learning algorithm to extract a near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy self-adaptive particle swarm optimization vector quantization (FSAPSOVQ) learning schemes, combined advantages of the fuzzy inference method (FIM), the simple VQ concept and the efficient s...

Fuzzy clustering algorithms like the popular fuzzy cmeans algorithm (FCM) are frequently used to automatically divide up the data space into fuzzy granules (fuzzy vector quantization). In the context of fuzzy systems, in order to be intuitive and meaningful to the user, the fuzzy membership functions of the used linguistic terms have to fulfill some requirements like boundedness of support or u...

An electronic nose system had been developed by using 16 quartz resonator sensitive membranesbasic resonance frequencies 20 MHz as a sensor, and analyzed the measurement data through various neural network as a pattern recognition system. The developed system showed high recognition probability to discriminate various single odors even mixture odor to its high generality properties; however the...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

Vector quantization is very efficient for data compression of speech and image. The channel distortions are introduced due to channel noise. Assigning suitable indices to codevectors can reduce distortion due to an imperfect channel. Several codebook index assignment algorithms were proposed. Unfortunately, no algorithm is always better than the others for any bit error rate due to these algori...

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, This is a hybrid of two product code vector quantization techniques namely the Multi stage vector quantization technique, and Switched split vector quantization technique,. Multi Switched Split Vecto...

Clustering analysis often employs unsupervised learning techniques originally developed for vector quantization. In this framework, a frequent goal of clustering systems is to minimize the quantization error, which is aaected by many local minima. To avoid connnement of reference vectors to local minima of the quantization error and to avoid formation of dead units, hard c-means clustering algo...