Abstract Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary effectively manage resources. We develope unsupervised modeling approach characterize cluster hydrographs on regional scale according dynamics. apply feature-based improve the exploitation heterogeneous ...

In this paper we analyze the convergence properties of a class of self-organizing neural networks, introduced and popularized by Kohonen, using the ODE approach. It is shown that Kohonen's learning law converges to the best locally a ne feature map. A new integrally distributed self-organizing learning law which converges to the equiprobable feature map for inputs which have arbitrary random pr...

1. INTRODUCTION Self-organizing neural networks represent a family of useful clustering-based classification methods in several application domains. One such technique is the Kohonen Self-Organizing Feature Map (SOM) (Kohonen,

In this work the classification of Force Expiratory volume in 1 second (FEV 1) in pulmonary function test is carried out using Spirometer and Self Organizing Feature Map Algorithm. Spirometry data are measure with flow volume spirometer from subject (N=100 including Noramal, and Abnormal) using standard data acquisition protocol. The acquire data are then used to classify FEV1. Self Organizing ...

The problem of image segmentation can be formulated as one of vector quantization. Although self-organizing networks with competitive learning are useful for vector quantization, they, in their original single-layer structure, are inadequate for image segmentation. This paper proposes and describes a hierarchical self-organizing neural network for image segmentation. The hierarchical self-organ...

In this article, the use of the self-organizing map (SOM) is approached on the basis of current theories of learning. Possibilities of computer and networked platforms that aim at helping human learning are also inspected. It is shown how the SOM can be considered a model of constructive learning. The area of constructive learning is outlined and two cases of using the self-organizing map in co...

The self-organizing map is discussed as an unsupervised clustering method. Its ability to form clusters indicates similar features in a data set. Based on this property, it is demonstrated that a self-organizing map is capable of identifying features within software code by grouping procedures with similar properties together. This allows us to identify potential objects, abstract data types or...