• 응용통계연구
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• 제37권3호
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• pp.297-309
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• 2024

본 논문에서는 자기회귀 신경망 모형과 지수평활법을 결합(NNARX+ETS 모형)하고 그 성능을 평가한다. 제안된 결합 모형은 시계열 자료를 예측하기 위하여 NNARX 모형의 외생변수로서 ETS 모형의 구성 성분을 활용한다. 이 모형의 주요 아이디어는, 신경망 모형이 원시계열 자료의 과거 시차만을 고려하는 것을 한계를 넘어서서 전통적 시계열 예측 방법인 지수평활법에 의해서 추출된 정제된 시계열 구성 성분까지도 추가로 신경망 모형의 입력값으로 사용하는 것이다. 예측 성능 평가는 2가지 실제 시계열 자료를 사용하였으며 제안된 모형을 NNAR 모형 및 전통적 시계열 분석 방법인 ETS와 ARIMA 모형과 비교하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.732-735
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• 1999

A GA-based optimization technique is adopted in the paper to obtain optimal future control inputs for predictive control systems. For reliable future predictions of a process, we identify the underlying process with an NNARX model structure and investigate to reduce the volume of neural network based on the Lipschitz index and a criterion. Since most industrial processes are subject to their constraints, we deal with the input-output constraints by modifying some genetic operators and/or using a penalty strategy in the GAPC. Some computer simulations are given to show the effectiveness of the GAPC method compared with the adaptive GPC algorithm.

• Structural Engineering and Mechanics
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• 제50권1호
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• pp.105-119
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• 2014

This study presents a steel container crane movement analysis and assessment based on structural health monitoring (SHM). The accelerometers are used to monitor the dynamic crane behavior and a 3-D finite element model (FEM) was designed to express the static displacement of the crane under the different load cases. The multi-input single-output nonlinear autoregressive neural network with external input (NNARX) model is used to identify the crane dynamic displacements. The FEM analysis and the identification model are used to investigate the safety and the vibration state of the crane in both time and frequency domains. Moreover, the SHM system is used based on the FEM analysis to assess the crane behavior. The analysis results indicate that: (1) the mean relative dynamic displacement can reveal the relative static movement of structures under environmental load; (2) the environmental load conditions clearly affect the crane deformations in different load cases; (3) the crane deformations are shown within the safe limits under different loads.