Radial basis function process neural network training based on generalized frechet distance and GA-SA hybrid strategy
This is an incremental improvement for optimizing neural network training in time-varying function learning.
The paper tackled the training problem of Radial Basis Function Process Neural Networks by proposing a hybrid GA-SA optimization method that uses generalized Fréchet distance to convert function learning into discrete sequence training, resulting in improved efficiency and stability.
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 Fréchet 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.