A Genetic-Algorithm-Based Two-Stage Learning Scheme for Neural Networks

In this paper, we propose A two-stage learning scheme for neural networks by integrating Gas into Structure identification In the first stage, which is also called structure identification stage, the selection of network structure and initial parameters is carried out by float genetic algorithm instead of human ln the second stage which is called parameter identification stage the conventional optimization method is adopted to make refinements of parameters. Through the entire process, compromise is satisfactorily made among the network complexity, approximation accuracy and generalization ability.