M. Winter

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In the classical neural-gas method, the neurons are located in the input space according to the input density. But if we want to use this kind of approach as a function approximator, it can be interesting to change the spatial distribution of the neurons, so that they concentrate in regions where the unknown function appeared to be more complex. To achieve(More)
We propose a new neural approach for approximating function using a reinforcement-type learning: each time the network generates an output, the environment responds with the scalar distance between the delivered output and the expected one. Thus, this distance is the only information the network can use to modify the estimation of the multi-dimensional(More)
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