This work presents a neural network model for the clustering analysis of data based on Self Organizing Maps (SOM). The model evolves during the training stage towards a hierarchical structure according to the input requirements. The hierarchical structure symbolizes a specialization tool that provides refinements of the classification process. The structure behaves like a single map with differ...

Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen’s Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or characte...

In this paper it is presented a framework environment for neural network simulation through the development of e-Services running on the EGEE Grid infrastructure. Parallel computing and distribution of jobs can be synchronized by the e-Service through a web interface. This interface provides an easy to use environment and can be requested over the internet. This simulation tool is based on a ne...

With the growing rate of network attacks, intelligent methods for detecting new attacks have attracted increasing interest. The RT-UNNID system, introduced in this paper, is one such system, capable of intelligent real-time intrusion detection using unsupervised neural networks. Unsupervised neural nets can improve their analysis of new data over time without retraining. In previous work, we ev...

The paper presents a method for times series prediction using a local dynamic modeling based on a three step process. In the first step the input data is embedded in a reconstruction space using a memory structure. The second step, implemented by a self-organizing map (SOM), derives a set of local models from data. The third step is accomplished by a set of functional networks. The goal of the ...

In this paper a novel method to solve the fine synchronization problem in GNSS receivers is presented. In particular a hierarchical neural network-based solution, able to estimate the channel in which the receiver operates, will be shown. The proposed method is based on two different Neural Networks and it is able to improve the fine tracking performances in urban environment. The solution take...

We propose a new recognition model called Concurrent Neural Networks (CNN), representing a winner-takes-all collection of neural networks. Each network of the system is trained individually to provide best results for one class only. We have applied the above model for the task of speaker recognition. We performed distinct speaker recognition experiments using three variants of basic components...

Building a model to rapidly simulate the impact of typhoons on agriculture and to predict agricultural losses is crucial and great help for remedial measure and distributing subvention right after the disaster. The relationship between typhoon-related meteorological factors and agricultural losses was first evaluated, and the Pearson’s test was applied to find consequences of both landfall and ...

In this paper, a new powerful method in artificial neural networks, called modular network SOM (mnSOM) is introduced. mnSOM is a generalization of Self Organizing Maps (SOM) formed by replacing each vector unit of SOM with function module. The modular function could be a multi layer perceptron, a recurrent neural network or even SOM itself. Having this flexibility, mnSOM becomes a new powerful ...

Neural networks have been traditionally considered as an alternative approach to pattern recognition in general, and speech recognition in particular. There have been much success in practical pattern recognition applications using neural networks including multi-layer perceptrons, radial basis functions, and self-organizing maps (SOMs). In this paper, we propose a system of SOMs based on the a...