  For learning problem of Radial Basis Function Process Neural Network
(RBF-PNN), an optimization training method based on GA combined with SA is
proposed in this paper. Through building generalized Fr\'echet distance to
measure similarity between time-varying function samples, the learning problem
of radial basis centre functions and connection weights is converted into the
training on corresponding discrete sequence coefficients. Network training
objective function is constructed according to the least square error
criterion, and global optimization solving of network parameters is implemented
in feasible solution space by use of global optimization feature of GA and
probabilistic jumping property of SA . The experiment results illustrate that
the training algorithm improves the network training efficiency and stability.
