A decision approach to mechanism type selection is presented, which employs LVQ neural network as classifier and decision-maker to recognize a satisfactory mechanism from a range of mechanisms achieving a required kinematic function. Through learning from correct samples extracted from different mechanisms, expert knowledge is acquired and expressed in the form of weight matrix by LVQ network. ...

We present a regularization technique to extend recently proposed matrix learning schemes in Learning Vector Quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can display a tendency towards over-simplification in the course of training. An overly pronounced elimination of dimensions...

Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype-based clustering algorithm. Generalized relevance LVQ (GRLVQ) constitutes a modification which obeys the dynamics of a gradient descent and allows an adaptive metric utilizing relevance factors for the input dimensions. As iterative algorithms with local learning rules, LVQ and ...

Lattice vector quantization (LVQ) reduces coding complexity and computation due to its regular structure. A new multistage LVQ (MLVQ) using an adaptive subband thresholding technique is presented and applied to image compression. The technique concentrates on reducing the quantization error of the quantized vectors by “blowing out” the residual quantization errors with an LVQ scale factor. The ...

Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from a single neuron solving linearly separable problems, to multithreshold neuron solving k-separable problems, to neurons implementing prototypes solving q-separable problems, is investigated. Using Learning Vector Quant...

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and situation assessment an important part of it. To overcome shortcomings existing methods, such as low accuracy strong dependence on prior knowledge, data-driven method proposed. clustering classification are combined, former used to mine situational latter realize rapid assessment. Angle evaluation factor distanc...

In this paper we consider a feature extraction approach for recognition of handwritten electrical symbols. The symbols are represented as a sequence of points. We apply a feature extraction technique to extract the most important features and then feed them for recognition to a Neural Network. We utilize a Learning Vector Quantization (LVQ) network and show its capability to recognize the symbo...

Lattice vector quantization (LVQ) reduces computational load and design complexity due to its regular structure. In this letter, we introduce and analyze the performance of two hybrid combinations of two lattices i.e. the AnAn and AnDn. Experiment results show that multistage LVQ with lattice AnA combination in four dimensional vector offers the least quantization errors with p = 0.0098 as comp...

This paper presents a real-time hand gesture recognizer based on a Learning Vector Quantization (LVQ) classifier. The recognizer is formed by two modules. The first module, mainly composed of a data glove, performs the feature extraction. The second module, the classifier, is performed by means of LVQ. The recognizer, tested on a dataset of 3900 hand gestures, performed by people of different g...

In this study, two kinds of neural networks are employed to develop a supervised ECG beat classifier. In order to improve the performance of the MLP classifier for application to ECG signal, the performance is compared to an LVQ neural network classifier. The two classifiers are tested with selected ECG time series and experimental results show that the MLP classifier offers a great potential i...