Tibor Fomin

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A fully self-organizing neural network approach to low-dimensional control problems is described. We consider the problem of learning to control an object and solving the path planning problem at the same time. Control is based on the path planning model that follows the gradient of the stationary solution of a diffusion process working in the state space.(More)
Self{organizing neural networks with Hebbian and anti{Hebbian learning rules were found robust against variations in the parameters of neurons of the network, such as neural activities, learning rates and noisy inputs. Robustness was evaluated from the point of view of properties of soft competition for input correlations. Two models were studied: a neural(More)
A set of scaling feedforward filters is developed in an unsupervised way via inputting pixel-discretized extended objects into a winner-take-all artificial neural network. The system discretizes the input space by both position and size. Depending on the distribution of input samples and below a certain number of neurons the spatial filters may form groups(More)
Competitive learning algorithms are statistically driven schemes requiring that the training samples are both representative and randomly ordered. Within the frame of self-organization, the latter condition appears as a paradoxical unrealistic assumption about the temporal structure of the environment. In this paper, the resulting vulnerability to(More)
  • T Fomin, J Kk Ormendy-Rr, A Acz, L} Orincz
  • 1997
A dynamic connectionist data compression and reconstruction (DCR) network is introduced. The network features fast learning capabilities, dynamic feedback of the output to the input, and apparent competition. It is shown that the data reconstruction procedure of the DCR network is equivalent to Wittmeyer's iterative method. Comparisons with a soft(More)
Abst"'uct-Self-organizing neural network solutions to control problems are described. Conlpetitive networks �reate spatial filters and geonletry connedion� in a �elf-organizing fa�h­ ion. The goal position, the obstacles and the object under control all create neural activi­ ties through the filters. Spreading activation that discriminates between the(More)
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