| Abstract: | System reliability optimization problems such as redundancy allocation are hard to solve exactly. Neural networks offer an alternative computational model for obtaining good approximate solutions for such problems. In this paper we present a neural network for solving the redundancy allocation problem for a n-stage parallel redundant system with separable objective function and constraints. The problem is formulated as a 0–1 integer programming problem and solved using the network. The performance of the network compare favourably with that of the best fit algorithm. The number of iterations taken by the network increases very slowly with increase in number of variables. Hence the network can easily solve large problems. |
