We propose a detection and classification system for various road situations, which is robust to light changes and different road markings. The road curves in an image are described with a Hough Transform, where histograms accumulate the contrast lines for each pixel. The resulting 2D histograms are used to train a Kohonen Neural Network. The final output classification can be used to improve r...

This paper presents a new algorithm for the deinterleaving of radar signals, based on the direction of arrival (DOA), carrier frequency (RF), and time of arrival (TOA). The algorithm is applied to classic (constant), jitter, staggered, and dwell switch pulse repetition interval (PRI) signals. This algorithm consists of two stages. In the first stage, a Kohonen neural network clusters the receiv...

The cluster analysis represents a group of methods whose aim is to classify the investigated objects into clusters. There have been suggested many new algorithms recently. This article deals with the use of an advanced method of neural network represented by Kohonen self-organizing maps for cluster analysis and describes its basis. The software Matlab 7.1 was used to present the applications of...

The purpose of this paper is to present a neuro fuzzy approach to the problem of automatic recognition of human faces. This approach is based on a Kohonen neural network (ANN), which we have trained, in unsupervised way, using a fuzzy competitive learning algorithm previously designed, implemented and tested on real images. Illustrative examples that demonstrate the effectiveness of this approa...

To overcome the effect of temperature on laser gyro zero bias and to stabilize the laser gyro output, this study proposes a modified radial basis function neural network (RBFNN) based on a Kohonen network and an orthogonal least squares (OLS) algorithm. The modified method, which combines the pattern classification capability of the Kohonen network and the optimal choice capacity of OLS, avoids...

A nonlinear projection method is presented to visualize high-dimensional data as a 2D image. The proposed method is based on the topology preserving mapping algorithm of Kohonen. The topology preserving mapping algorithm is used to train a 2D network structure. Then the interpoint distances in the feature space between the units in the network are graphically displayed to show the underlying st...

,4bstract: A new learning algorithm is proposed for the training of single layer linear networks. The network studied has an input layer of N units and an output layer of M units. The input and output layer are fully connected via a n M x N weighit matrix. It is well known that such a network of linear processing units will generate M principal components of the input distribution when it is tr...

We present a new self-organizing neural network which performs unsupervised learning and can be used for vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process which also includes occasional removal of units. The ...

The modern world is impossible to imagine without computer networks: both local and global; therefore, the issue of network security becoming increasingly topical. Currently, methods detecting attacks can be strengthened by using neural networks, which confirms relevance topic. aim study a comparative analysis quality parameters combined variant consisting different networks. As research used: ...