A global optimization algorithm for neural network training

The main thrust of the research is to develop a global optimization algorithm tailored for multilayer feedforward back-propagation neural network training. The goal in designing the algorithm is to tackle the problem of reaching nonoptimal network configurations due to being trapped by a saddle point or a local minimum so that continuous learning through automatic online retraining is feasible.