| 학회 | 한국화학공학회 |
| 학술대회 | 2006년 봄 (04/20 ~ 04/21, 대구 인터불고 호텔) |
| 권호 | 12권 1호, p.132 |
| 발표분야 | 공정시스템 |
| 제목 | Self-organizing algorithms for artificial neural networks for high-throughput screening system |
| 초록 | During the last several years, the development of combinatorial chemistry has enabled synthesis of a huge amount of chemical compounds in a short time. Therefore HTS (High-throughput screening) is required for dealing with the enormous materials. But human intervention (trial & error method) in data mining of experimental results lowers the efficiency of HTS. So self-organizing neural networks that rapidly and accurately transact experimental results are needed for the improvement in HTS performance. The self-organizing algorithms that were previously developed have randomness which causes unrealiability of algorithms which means different trials give quite different performances. However, in the proposed algorithm, randomness of neural networks is effectively eliminated by the optimized construction with hidden-neuron and hidden-layer addition. So this algorithm always matches the complexity of the model to that of the problem very well. As a result, this algorithm can find a near-optimal network which is compact and shows good generalization performance without human intervention. Acknowledgement: This work is supported by the BK21 Project and Center for Ultramicrochemical Process Systems sponsored by KOSEF. |
| 저자 | 강수길, 박선원 |
| 소속 | 한국과학기술원 |
| 키워드 | neural networks; high-throughput screening; combinatorial chemistry |
| 원문파일 | 초록 보기 |
