Implementation and Comparison of Three Neural Network Learning Algorithms

Abstract

The performance of a welding process determines not only the cost, but also the quality of the product. How to control the welding process in order to ensure good welding performance with less cost and higher Productivity has become critical. The objective of this study is twofold: (1) developing artificial neural networks to predict welding performance using different learning algorithms: back propagation, simulated annealing and tabu search; (2) comparing and discussing the performance of neural networks trained using those algorithms. Statistical analysis shows that back propagation is able to make more accurate prediction than the other algorithms for this particular application. However, all three algorithms demonstrate impressive flexibility and robustness.

Citation

Huang, T., Zhang, C., Lee, S. and Wang, H.(B). (1993), "Implementation and Comparison of Three Neural Network Learning Algorithms", Kybernetes, Vol. 22 No. 1, pp. 22-38. https://doi.org/10.1108/eb005954