| Title |
A Study on the Structure Optimization of Multilayer Neural Networks using Rough Set Theory |
| Authors |
정영준(Chung, Young-June) ; 전효병(Jun, Hyo-Byung) ; 심귀보(Sim, Kwee-Bo) |
| Keywords |
Neural networks ; Back-propagation learning ; Hyperplane ; Rough set |
| Abstract |
In this paper, we propose a new structure optimization method of multilayer neural networks which begin and carry out learning from a bigger network. This method redundant links and neurons according to the rough set theory. In order to find redundant links, we analyze the variations of all weights and output errors in every step of the learning process, and then make the decision table from their variation of weights and output errors. We can find the redundant links from the initial structure by analyzing the decision table using the rough set theory. This enables us to build a structure as compact as possible, and also enables mapping between input and output. We show the validity and effectiveness of the proposed algorithm by applying it to the XOR problem. |