We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions toward an average opinion, whereas low thresholds result in several opinion clusters. The model is further generalized to network interactions

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We study an algorithm for a feedforward network which is similar in spirit to the Tiling algorithm recently introduced: the hidden units are added one by one until the network performs the desired task, and convergence is guaranteed. The difference is in the architecture of the network, which is more constrained here. Numerical tests show performances similar to that of the Tiling algorithm, although the total number of couplings in general grows faster.