Hopfield network based approach to aircraft design

Combining augmented lagrange multiplier (ALM) method to Hopfield neural network (HNN), was proposed to solve nonlinear constrained optimization. HNN is taken as a dynamic approach for minimization subproblem in ALM method, only needing the first derivative of the Lagrange function. The random neural network was extended by adding Gaussian noise gradually reducing with the temperature, whose ability escaping from the attraction of the localminimum points was limited by the initial temperature. Combined with the simulated annealing, an improved algorithm for Euler method was presented for numerical implementation of the network. The approach was applied to jet trainer aircraft preliminary optimization. The results show that the computing process is stable and the optimal result is enough precise. The trade-off of design requirements was studied through the Lagrange multipliers. An aerodynamic/structural design optimization for the wing of a trunkeliner was studied.