For systems with multiple redundancies, reliability evaluation in the redundancy allocation problem (RAP) constitutes a computational complexity. It has been demonstrated that neural network training provides an efficient approach to estimate the complex system reliability function. When executing the neural network algorithm, there are many parameters that need to be determined for improving the training performance. Therefore, robust experimental design method can be used to determine the neural network parameters. The traditional robust design methods are intended for a single response variable. However, the application of neural network method includes more than one measurement, such as estimation accuracy and time efficiency. In this paper, utility function is first estimated by neural network training, in which the algorithm parameters are determined by weighted principal component (WPCA)-based multi-response optimization which simultaneously optimizes more than one training performance measurements. Moreover, it is always desirable to simultaneously optimize several objectives in designing a system, such as reliability, cost, etc. Therefore, continuous WPCA-based multi-response design is then applied to obtain the best design of redundancies in RAP, which simultaneously optimize multiple objectives by taking into account the correlations between them.
Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm
