This paper proposes a novel adaptive group organization cooperative evolutionary algorithm (AGOCEA) for TSK-type neural fuzzy networks design. The proposed AGOCEA uses group-based cooperative evolutionary algorithm and selforganizing technique to automatically design neural fuzzy networks. The group-based evolutionary divided populations to several groups and each group can evolve itself. In th...

Granular computing is a computational paradigm in which a granule represents a structure of patterns evolved by performing operations on the individual patterns. Two granular neural networks are described for performing the pattern analysis tasks like classification and clustering. The granular neural networks are designed by integrating fuzzy sets and fuzzy rough sets with artificial neural ne...

This paper deals with design of optimal structure of Kohonen Self-organizing maps for cluster analysis applications. The cluster analysis represents a group of methods whose aim is to classify the objects into clusters. There have been many new algorithms solving cluster analysis applications, which used neural networks. This paper deals with the use of advanced methods of neural networks repre...

The counter-propagation neural networks have been widely used by the chemometricians for more than fifteen years. This valuable tool for data analysis has been applied for solving many different chemometric problems. In this paper the implementation of counter-propagation neural networks in Matlab environment is described. The program presented here is an extension of Self-Organizing Maps Toolb...

We present Turing's neural-network-like structures (unorganized machines) and compare them to Kau man's random boolean networks (RBN). Some characteristics of attractors are brie y presented. We then apply a self-organizing topology evolving algorithm to Turing's networks and show that the network evolves towards an average connectivity of KC = 2 for large systems (N !1).

Analysing high-dimensional data is a task where software tools can reasonably assist the data analyst, by visualising, and thereby uncovering, the inherent structure and topology of the data collection. Especially the kinds of tools that can produce results autonomously, i.e. unsupervised tools, are a goal; here, neural network models may be one solution. In the category of unsupervised neural ...