The bandwidth reduction or storage lowering in digital image transmission confers to the image compression a key role. In this paper, we propose a new approach for lossy image compression: the source image is vector quantized by applying Self-Organizing Map (SOM) with several dictionaries. Each dictionary is originally designed based on the feature vectors resulted after applying the Walsh-Hada...

This paper reports application of neurofuzzy inference system (NFIS) and self organizing feature map neural networks (SOM) on detection of contact state in a block system. In this manner, on a simple system, the evolution of contact states, by parallelization of DDA, has been investigated. So, a comparison between NFIS and SOM results has been presented. The results show applicability of the pr...

The classification problem of determining if a surveillance camera sees persons is tackled with two neural models: the Self-Organizing Map (SOM) with supervision as in a classical conditioning analogy and Multi Layer Perceptrons (MLP). The first model, that we call Conditioning-SOM (C-SOM) allowed a quick selection of input features with a good tradeoff between computational cost and classifica...

The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to ana...

The paper starts with the need for classification. Then the reasons why neural networks are suitable for document classification are explained. The paper continues with the details of the most commonly used topologically organized network model proposed by Kohonen (1982), referred to as the self-organizing map (SOM). The general idea proposed is to display the contents of a document library by ...

Recently, security of the computer systems becomes an important problem. Almost all computers use the password mechanism for the user authentication. But password mechanism has many issues. In this paper, we propose a kind of biometrics authentication method using the combinations of key stroke timings and pen calligraphy. For this method, selection of the phrase is important. We analyzed the k...

The Self-Organizing Map (SOM) is one of the popular Artificial Neural Networks which is a useful in clustering and visualizing complex high dimensional data. Conventional SOMs are based on the two-dimensional (2D) grid structure, which usually results in less accurate representation of the data. Several SOMs using spherical data structures have been proposed to remove the “border effect”. In th...

As a typical combinatorial optimization problem, the traveling salesman problem (TSP) has attracted extensive research interest. In this paper, we develop a self-organizing map (SOM) with a novel learning rule. It is called the integrated SOM (ISOM) since its learning rule integrates the three learning mechanisms in the SOM literature. Within a single learning step, the excited neuron is first ...

Artificial Intelligence (AI) has recently been recognized as a worthwhile tool for supporting manufacturing operations. This paper reviews AI-related approaches to Group Technology (GT) and presents the Self-Organizing Map (SOM) network, a special type of neural networks, as an intelligent tool for grouping parts and machines. SOM can learn from complex, multi-dimensional data and transform the...

Large datasets can be analyzed through different linear and nonlinear methods. Most frequently used linear method is Principal Component Analysis (PCA) known also as EOF (Empirical Orthogonal Function) analysis, permitting both clustering and visualizing high-dimensional data items. However, many problems are nonlinear in nature, so, for analyzing such a problems some nonlinear methods will be ...