A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.
Geometrically nonlinear stiffness matrix due to large displacement small strain was firstly formulated explicitly for the basic components of pantographic foldable structures,namely, the uniplet, derived from a three node beam element.The formulation of the uniplet stiffness matrix is based on the precise nonlinear finite element theory and the displacement harmonized and internal force constraints are applied directly to the deformation modes of the three node beam element. The formulations were derived in general form, and can be simplified for particular foldable structures, such as flat, cylindrical and spherical structures.Finally, two examples were presented to illustrate the applications of the stiffness matrix evolved.
