In this study, we try to implant chaotic features into the learning algorithm of self-organizing map. We call this concept as Chaotic SOM (CHAOSOM). As a first step to realize CHAOSOM, we consider the case that learning rate and neighboring coefficient of SOM are refreshed by chaotic pulses generated by the Hodgkin-Huxley equation. We apply the CHAOSOM to solve a traveling salesman problem and ...

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep sea, and expected as the attractive tool. However, AUVs have various problems which should be solved. In this paper, the Self-Organizing Map (SOM) is applied as the clustering method for the navigation system. The SOM is known as one of the effective methods to extract the principle feature from many parameters a...

Kohonen’s Self-Organizing Map is a neural network procedure in which a layer of neurons is initialized with random weights, and subsequently organized by inspection of the data to be classified. The organization procedure uses progressive adjustment of weights based on data characteristics and lateral interaction such that neurons with similar weights will tend to spatially cluster in the neuro...

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships wit...

We propose a neural preprocess approach for video-based gesture recognition system. Second-order neural network (SONN) and self-organizing map (SOM) are employed for extracting moving hand regions and for normalizing motion features respectively. The SONN is more robust to noise than frame difference technique. Obtained velocity feature vectors are translated into normalized feature space by th...

In this paper, a new approach to Mediterranean Water Eddy border detection is proposed. Kohonen self-organizing maps (SOM) are used as data mining tools to cluster image pixels through an unsupervised process. The clusters are visualized on the SOM internal map. From the visualization, the borders can be detected through an interactive way. As a result, interesting patterns are visible on the i...

This paper presents a combination of Self-Organizing Map (SOM) approach and navigation functions in the Traveling Salesman Problem with segment goals where paths between goals have to respect obstacles. Hence, the problem is called multi-goal path planning. The problem arises from the inspection planning, where a path from which all points of the given polygonal environment have to be “seen”. T...

The paper presents a new method for adaptive selection of Self-Organizing Map (SOM) self-training endpoint. A method is based on the estimation of the newly introduced parameter ∆init and the learning depth parameter κ. In order to propose an optimal range of κ values, the influence of the selected learning depth parameter to the performance of SOM was tested experimentally using input data wit...