Development of Repetitively Enhanced Neural Networks (RENN) for Efficient Missile Design and Optimization

Abstract

An improved approach for design optimization of air intercept missile is developed and presented. A Bayesian learning technique is mapped into Back-propagation neural networks (BPNN) to establish an accurate and effective system approximation, namely an enhanced neural network module. Then, the surrogate models are generated and sent to a hybrid optimizer in which a tentative optimum result is obtained and updated into the training data to refine the response surfaces. This process, which is called Repetitively Enhanced Neural Networks (RENN), is executed repeatedly to refine the response surface until the convergent optimum solution is obtained. A numerical example and a two-member frame design are presented and discuss to demonstrate the accuracy and feasibility of RENN. Eventually, this RENN approach is applied to re-design the air intercept missile-AIM