While back-propagation neural networks (BPNN) are effective learning tools for building non-linear models, they are often unstable when using small-data-sets. Therefore, in order to solve this problem, we construct artificial samples, called virtual samples, to improve the learning robustness. This research develops a novel method of virtual sample generation (VSG), named genetic algorithm-based virtual sample generation (GABVSG), which considers the integrated effects and constraints of data attributes. We first determine the acceptable range by using MTD functions, and construct the feasibility-based programming (FBP) model with BPNN. A genetic algorithm (GA) is then applied to accelerate the generation of feasible virtual samples. Finally, we use two real cases to verify the performance of the proposed method by comparing the results with those of two forecasting models, BPNN and support vector machine for regression (SVR). The experimental results indicate that the performance of the GABVSG method is superior to that of using original training data without artificial samples. Consequently, the proposed method can improve learning performance significantly when working with small samples.
|主出版物標題||Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013|
|出版狀態||Published - 2013|
|事件||2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 - Macau, China|
持續時間: 2013 十一月 15 → 2013 十一月 17
|Other||2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013|
|期間||13-11-15 → 13-11-17|
All Science Journal Classification (ASJC) codes