  Convergence rate of training algorithms for neural networks is heavily
affected by initialization of weights. In this paper, an original algorithm for
initialization of weights in backpropagation neural net is presented with
application to character recognition. The initialization method is mainly based
on a customization of the Kalman filter, translating it into Bayesian
statistics terms. A metrological approach is used in this context considering
weights as measurements modeled by mutually dependent normal random variables.
The algorithm performance is demonstrated by reporting and discussing results
of simulation trials. Results are compared with random weights initialization
and other methods. The proposed method shows an improved convergence rate for
the backpropagation training algorithm.
