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)
A set of scaling feedforward lters is developed in an unsupervised way via inputting pixel discretized extended objects into a winner-take-all artiicial 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 lters may form groups of(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)
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