摘要
The analytical simulation relationship has been found between energy of a Hopfield back error propagation neural network model and the conventional mechanical mass system model. Since the energy expression is in quadratic form, which is corresponding to a steady state of energy distribution among processing unit of the neural network, and it is proved as a positive definite problem. Through simulation, a “Hamilton principle like” energy expression is introduced and an additional condition of the steady state of neural network system can be formulated through certain transformations. These results can be served for speeding the convergence of machine learning and identification processes of the neural network systems.
