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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)

- T Rozgonyi, T Fomin, A L} Orincz
- 1994

- 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)

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)

- Tibor Fomin, Tam As Rozgonyi, Csaba Szepesv, Ari Y, Andr As, L Orincz
- 1997

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 diiusion process working in the state space.… (More)

- Tibor Fomin, Andrr
- 1994

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)

- S Tavitian, T Fomin, A L} Orincz Yz, A L} Orincz
- 2007

A step towards assumption-free self-organization is proposed. We address the problem of learning a stable representation of the environment from inputs continuously changing at an unpredictable rate. The vulnerability of competitive Hebbian learning to low rate changes is assessed. It is shown that anti-Hebbian suppression of the feed-forward Hebbian… (More)

- T. Fomin, A. Lorincz
- 1994

|Self-organizing neural network solutions to control problems are described. Competitive networks create spatial lters and geometry connections in a self-organizing fashion. The goal position, the obstacles and the object under control all create neural activities through the lters. Spreading activation that discriminates between the controlled object , the… (More)

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