Recurrent Neural Networks for Structural Optimization

This paper presents an improvement for an artificial neural network paradigm that has shown a significant potential for successful application to a class of optimization problems in structural engineering. The artificial neural network paradigm includes algorithms that belong to the class of single-layer, relaxationtype recurrent neural networks. The suggested improvement enhances the convergence performance and involves a technique that sets the values of weight parameters of the recurrent neural network algorithm. The complete procedure of solving an optimization problem with a single-layer, relaxation-type recurrent neural network is introduced. The discrete Hopfield network is employed to solve the weighted matching problem. A set of simulation experiments is performed to analyze the performance of the discrete Hopfield network. Simulation results confirm that the discrete Hopfield network locates a locally optimum solution after each relaxation once the weight parameters are specified as defined in the suggested technique.