The present paper proposes an application of potentiality learning to supervised learning. The potentiality has been developed as a measure of the importance of components in the self-organizing maps (SOM) to extract important input neurons. The main characteristics lies in its simplicity and thus it can be easily implemented. If it is possible to use it for conventional supervised learning, be...

A system is described which takes synergies extracted from human grasp experiments and maps these onto a robot vision and hand-arm platform to facilitate the transfer of skills [1]. This system forms part of a framework which is extended by adding a self organizing map based affordance learning system. This affordance system learns the correlations between perceived object features and relevant...

A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behaviour is calculated analytically. For the original variant a magnification exponent of 4/7 is derived; the generalized version allows to steer the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional ...

In this paper a new method based on the self-organizing map (SOM) is proposed to track and identify changes in the dynamic behaviour of a physical process. In a first stage, a SOM is trained on a parameter space composed of the coefficients of local dynamic models estimated around different operating points of the process. On execution, new models estimated from process data are compared agains...

The Self-Organising Map (SOM) is a well-known neuralnetwork model that has successfully been used as a data analysis tool in many different domains. The SOM provides a topology-preserving mapping from a high-dimensional input space to a lower-dimensional output space, a convenient interface to the data. However, the real power of this model can only be utilised with sophisticated visualisations...

Resistance spot welding is used to join two or more metal objects together, and the technique is in widespread use in, for example, the automotive and electrical industries. This paper discusses both the identification of different spot welding processes and the process initialization parameters leading to highquality welding joints. In this research, self-organizing maps (SOMs) were used, and ...

Creating and studying neurocognitive architectures is an active and increasing focus of research efforts. Based on our recent research that uses neural activity limit cycles in selforganizing maps (SOMs) to represent external stimuli, this study explores the use of such limit cycle attractors in a neurocognitive architecture for an open-loop arm reaching task. The goal is to learn to produce a ...

In the present article, we attempt to devise a typology of forms of part-time employment by applying a widely used neuronal methodology called Kohonen maps. Starting out with data that we describe using category-specific variables, we show how it is possible to represent observations and the modalities of the variables that define them simultaneously, on a single map. This allows us to ascertai...

This paper discusses the application of a GH-SOM architecture to the problem of Handwritten Digit Recognition. The results proved to be better than the ones obtained from standard SOM networks.

Low-cost tin oxide gas sensors are inherently nonspecific. In addition, they have several undesirable characteristics such as slow response, nonlinearities, and long-term drifts. This paper shows that the combination of a gas-sensor array together with self-organizing maps (SOM’s) permit success in gas classification problems. The system is able to determine the gas present in an atmosphere wit...