• 한국산학기술학회논문지
• /
• 제16권10호
• /
• pp.7061-7070
• /
• 2015

다중반응표면 최적화는 다수의 반응변수(품질특성치)를 동시에 고려하여 최적의 입력변수 조건을 찾는 반응표면분석의 세부 분야이다. 가중평균제곱오차(Weighted Mean Squared Error, WMSE) 최소화법은 평균제곱오차의 두 구성 요소인 제곱편차와 분산에 가중치를 부여한 WMSE를 활용하는데, 반응변수별로 WMSE를 구하여 이들을 종합적으로 최소화한다. 지금까지 WMSE 최소화법과 관련하여 개발된 기법은 대부분 의사결정자의 선호도 정보를 문제풀이 이전에 결정할 것을 요구하는 선호도사전제시법에 해당된다. 그러나 현실적으로 의사결정자가 자신의 선호도 정보를 사전에 정확히 제공하는 것은 매우 어렵다. 본 논문에서는 이러한 한계점을 개선하기 위하여 WMSE 최소화를 위한 선호도사후제시법을 제안한다. 제안된 방법은 의사결정자의 선호도 정보 없이 다수의 비지배적해를 생성한 후, 의사결정자가 생성된 비지배해 중 최고선호해를 선택하는 단계로 진행된다. 제안된 방법은 의사결정자로 하여금 전체 해집합의 트레이드오프 관계를 보다 폭넓은 시각으로 이해한 후 선호도 정보를 제시할 수 있도록 함으로써, 의사결정자의 선호도에 부합하는 최고선호해를 효과적으로 도출할 수 있다.

• 한국통신학회논문지
• /
• 제28권10C호
• /
• pp.1007-1012
• /
• 2003

Multilayer perceptrons (MLPs)를 위한 일반적인 BP 알고리즘의 학습 속도를 개선하기 위하여 제약을 갖는 최적화 기술을 제안하고 이를 backpropagation (BP) 알고리즘에 적용한다. 먼저 잡음 제약을 갖는 LMS (noise constrained least mean square : NCLMS) 알고리즘과 영잡음 제약 LMS (ZNCLMS) 알고리즘을 BP 알고리즘에 적용한다. 이러한 알고리즘들은 다음과 같은 가정을 반드시 필요로 하여 알고리즘의 이용에 많은 제약을 갖는다. NCLMS 알고리즘을 이용한 NCBP 알고리즘은 정확한 잡음 전력을 알고 있다고 가정한다. 또한 ZNCLMS 알고리즘을 이용한 ZNCBP 알고리즘은 잡음의 전력을 0으로 가정, 즉 잡음을 무시하고 학습을 진행한다. 본 논문에서는 확장된(augmented) Lagrangian multiplier를 이용하여, 비용함수(cost function)를 변형한다. 이를 통하여 잡음에 대한 가정을 제거하고 ZNCBP와 NCBP 알고리즘을 확장, 일반화하여 적응 오류 제약 BP(adaptive error constrained BP : AECBP) 알고리즘을 유도, 제안한다. 제안한 알고리즘들의 수렴 속도는 일반적인 BP 알고리즘보다 약 30배정도 빠른 학습 속도를 나타내었으며, 일반적인 선형 필터와 거의 같은 수렴속도를 나타내었다.

• Geomechanics and Engineering
• /
• 제36권2호
• /
• pp.111-119
• /
• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• 한국자동차공학회논문집
• /
• 제7권6호
• /
• pp.198-206
• /
• 1999

An automobile outside rear view mirror system has been analyzed and designed to reduce vibration with a finite element model. model analysis is conducted for the calculation of natural frequencies. harmonic analysis is utilized to estimate the displacements of the glass surface under dynamic loads. The model is verified with the vibration experiment of the parts and the assembled body. The structure of the mirror system is optimized for the robustness defined by the Taguchi concept. At first, many potential design variables are defined. Final design variables are selected based on the amount of contribution on the objective function. That is, sensitive variables are chose. The SN ratio in the Taguchi method is replaced by an objective function with the mean and the standard deviation of the quality characteristic. The defined objective function is appropriate in the structural design in that the vibration displacements are minimized while the robustness is improved.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
• /
• pp.393-395
• /
• 2006

In competitive market, it is important to establish a plan of transmission expansion considering uncertainty of future generation and load behavior. For this reason, revised transmission expansion model is proposed in this paper. In the proposed model, information of predictable future condition are included in a cost function of transmission expansion investment. Also, to reduce risk of the investment, mean-variance Markowitz approach is added to the objective function of cost. By optimization programming, the most robust and the minimum cost plan can be obtained.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2003년도 춘계학술대회 논문집
• /
• pp.1362-1365
• /
• 2003

On this study. we improved the efficiency applying algorithm that is repeatedly using orthogonal array in discrete design space and filling a defect of gradient method in continuous design space. we showed optimal ply angle that maximized buckling strength of CFRP laminated composite plate without a hole and with a hole by each aspect ratio. In the case of CFRP laminated composite plate without a hole, we confirmed the reliance and efficiency of algorithm in comparison with the result optimization achievement repeatedly using statistical orthogonal array of experimental design.

• Communications for Statistical Applications and Methods
• /
• 제21권2호
• /
• pp.161-168
• /
• 2014

In mixture experiments with process variables, we consider the case that some of process variables are either uncontrollable or hard to control, which are called noise variables. Given the such mixture experimental data with process variables, first we study how to search for candidate models. Good candidate models are screened by the sequential variables selection method and checking the residual plots for the validity of the model assumption. Two methods, which use numerical optimization methods proposed by Derringer and Suich (1980) and minimization of the weighted expected loss, are proposed to find a cost-effective robust optimal condition in which the performance of the mean as well as the variance of the response for each of the candidate models is well-behaved under the cost restriction of the mixture. The proposed methods are illustrated with the well known fish patties texture example described by Cornell (2002).

• 한국소음진동공학회논문집
• /
• 제13권6호
• /
• pp.423-429
• /
• 2003

On this study. we improved the efficiency applying algorithm that is repeatedly using table of orthogonal array in discrete design space and filling a defect of gradient method in continuous design space. we showed optimal ply angle that maximized 1st natural frequency of CFRP laminated composite plate without a hole and with a hole by each aspect ratio. In the case of CFRP laminated composite plate without a hole, we confirmed the reliance and efficiency of algorithm in comparison with the result of optimization achievement repeatedly using statistical table of orthogonal array of experimental design and the BFGS optimal design method.

• 대한기계학회논문집A
• /
• 제37권8호
• /
• pp.975-981
• /
• 2013

강건 최적화 기법은 설계 초기 단계부터 설계변수의 변동이 목적함수에 미치는 효과를 최소화할 수 있는 유일한 방법이다. 강건 최적화의 정식화를 위해서는 분산을 정확히 예측하고 확률제한조건을 정식화하는 것이 가장 중요한 과정이 된다. 분산 및 확률제한조건을 예측하고 정식화하기 위한 방법으로 공정능력지수 및 식스시그마 기법과 같은 여러 가지 방법이 적용되고 있으나, 실제 공정에서 널리 적용되는 불량률을 이용한 확률제한조건 처리 기법에 대한 연구는 아직까지 전무한 상태이다. 본 연구에서는 자동차 로워암의 무게와 최대응력의 평균과 표준편차에 대한 설계영역을 탐색하고, 이후 로워암의 강건 최적화를 수행하였다. 변동을 예측하기 위한 표준편차의 계산은 2 차 테일러 전개를 통해 수치적인 정확도를 기하였다. 강건 최적화는 설계변수의 불연속성을 고려하기 위하여 최적화 과정에서 미분 정보를 적용하지 않은 심플렉스 알고리즘을 적용하였다.

• Industrial Engineering and Management Systems
• /
• 제4권1호
• /
• pp.54-67
• /
• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.