The Kohonen Self Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. Batch SOM algorithms based on adaptive and non-adaptive city-blo...

This paper presents a novel method to recognize inharmonic and transient bird sounds efficiently. The recognition algorithm consists of feature extraction using wavelet decomposition and recognition using either supervised or unsupervised classifier. The proposed method was tested on sounds of eight bird species of which five species have inharmonic sounds and three reference species have harmo...

The development of routing protocols for mobile ad hoc networks is a challenging and active research area, and a variety of approaches have been developed. Among them, geographic routing strategies use knowledge of the devices’ physical positions to achieve efficient routing performance – a critical goal for networks of resource-constrained devices. Strategies have also been developed to perfor...

The article presents neural network for recognition of driving strategies based on interactions between drivers in road traffic. It analyzes the architecture model implemented as a self-organizing map (SOM), consisting group networks radial basis functions (RBF). is training grounded biological foundations artificial networks, which set should consist exclusively input vectors; wherein algorith...

The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects highdimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. Despite the popular use of the algorithm for clustering and information visualisation, a system has been lacking that combines the fast ...

Autonomous Underwater Vehicles (AUVs) are attractive tools to survey earth science and oceanography, however, there exists a lot of problems to be solved such as motion control, acquisition of sensor data, decisionmaking, navigation without collision, self-localization and so on. In order to realize useful and practical robots, underwater vehicles should take their action by judging the changin...

Fatigued bills have harmful influence on the daily operation of Automated Teller Machine(ATM). To make the classification of fatigued bills more efficient, the development of an automatic fatigued bill classification method is desirable. We propose a new method to estimate the fatigue level of bill from the feature-selected frequency band acoustic energy pattern of banking machines. By using a ...

The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable ...

Models are abstractions of observed real world phenomena or processes. A good model captures the essential properties of the modeled phenomena. In the statistical learning paradigm the processes that generate observations are assumed unknown and too complex for analytical modeling, thus the models are trained from more general templates with measured observations. A substantial part of the proc...

This paper presents an approach to the well-known Traveling Salesman Problem (TSP) via competitive neural networks. The neural network model adopted in this work is the Kohonen Network or Self-Organizing Maps (SOM), which has important topological information about its neurons configuration. This paper is concerned with the initialization aspects, parameters adaptation, and the complexity analy...