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For learning problem of Radial Basis Function Process Neural Network (RBFPNN), an optimization
training method based on GA combined with SA is proposed in this paper. Through building generalized
Fréchet distance to measure similarity between timevarying 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.
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