실수코드 유전알고리즘과 인공신경망을 이용한 가스터빈 엔진의 복합 결함 진단 연구

In this study, Real Coded Genetic Algorithm(RCGA) and Artificial Neural Network(ANN) are used for developing the defect diagnostics of the aircraft turbo-shaft engine. ANN accompanied with large amount data has a most serious problem to fall in the local minima. Because of this weak point, it becomes very difficult to obtain good convergence ratio and high accuracy. To solve this problem, GA based ANN has been suggested. GA is able to search the global minima better than ANN. GA based ANN has shown the RMS defect error of 5% less in single and dual defect cases.