Abstract: From the view point of construction, research on dynamic structure design of neural network is carried out and a dynamic structure design method of multilayer feedforward network (DYNSDMFN) based on generalization performance is proposed and the corresponding calculation program is worked out. Based on Ockhams RAZOR principle, DYNSDMFN starts from a basic architecture and designed structure of MFN dynamically, corresponding to given training sample set and test sample set, by means of adding new neuron of hidden layer or new hidden layer dynamically, comphrehensive application of several improving methods of generalization performance, an improved BP algorithm, the determination method of learning ratio, momentum coefficient, jumping factor and regularization coefficient adopting the combined searching mechanism in quick and global manner, the combination of local and global weight adjustment project, dynamic structure design of multilayer feedforward network is carried out The application results in the flanging springback prediction indicates that the network designed by using that method has a relative high calculation accuracy.

Key words: Springback prediction, Dynamic structure design, Generalization performance, Multilayer feedforward network, Topology structure