• 제목/요약/키워드: Approximation algorithm

검색결과 981건 처리시간 0.042초

근사알고리즘을 적용한 금형온도 제어 방법 (Mold temperature control method using Approximation Algorithm)

  • 박성수;구형일
    • 한국정보통신학회:학술대회논문집
    • /
    • 한국정보통신학회 2017년도 추계학술대회
    • /
    • pp.270-273
    • /
    • 2017
  • 플라스틱 사출물의 불량 감소 및 사이클 타임 축소를 통한 생산성 향상은 사출업계의 오랜 숙원 사항이다. 특히 중국 등 후발 주자의 추격과 좁혀지지 않는 독일, 일본과의 기술격차 사이에 끼어 있는 국내 사출업계에게 생산성 향상은 매우 절실하다. 30여년 국내 사출업계의 연구와 경험을 통해 금형 내 사출물 표면 온도 제어가 품질 관리의 핵심임을 알게 되었고 PID 제어 등 고급제어 기법을 활용한 다양한 시도가 있었으나 독일, 일본의 유수 업체의 생산성에는 아직 부족하다. 이에 근사알고리즘 중 "Knapsack"개념과 "Minimum Makespan Scheduling"기법을 활용하여 PID 제어로 풀기 어려운 수렴하지 않고 주기적인 반복 데이터 패턴을 지닌 대상을 효율적으로 제어할 수 있는 방법을 소개하고 또한 실제 사출 현장에서 추출한 데이터 분석으로 사출품의 생산성 향상에 근사알고리즘을 이용한 제어가 충분히 효과적임을 제시하고자 한다.

  • PDF

적응 파라미터 예측을 위한 근사화된 RLS 알고리즘 (An Approximated RLS Algorithm for Adaptive Parameter Estimation)

  • 안봉만;황지원;유정래;조주필
    • 한국통신학회논문지
    • /
    • 제32권9C호
    • /
    • pp.922-928
    • /
    • 2007
  • 본 논문은 근사화 기법을 RLS 알고리즘에 적용한 고속 적응 알고리즘을 제안한다. 제안 알고리즘(D-RLS)은 QR 분해 RLS 알고리즘 유도 과정을 RLS 알고리즘으로부터 역으로 유도한 알고리즘이다. 유도된 알고리즘(D-RLS)은 입력 신호들이 서로 분리되어 있다는 가정을 사용한 알고리즘과 유사한 형태를 취한다. 이 알고리즘의 계산량은 $O(N^2)$ 보다 작은 O(N)이다. 이 알고리즘의 성능 평가를 위하여 FIR 시스템과 비선형(Volterra) 시스템의 시스템 식별 기법을 이용하였으며, 결과적으로 우수한 성능을 나타냄을 확인하였다.

Non-redundant Successive Approximation Register를 적용한 A/D 변환기의 설계 (Design of A/D convertor adopting Non-redundant Successive Approximation Register)

  • 이종명;유재우;김범수;김대정
    • 대한전자공학회:학술대회논문집
    • /
    • 대한전자공학회 2006년도 하계종합학술대회
    • /
    • pp.523-524
    • /
    • 2006
  • Successive approximation A/D converters have an advantage of small chip area and simple algorithm. We propose an improved non-redundant successive approximation register (SAR) which can be incorporated in successive approximation A/D converters. The proposed SAR validates the preset state as the $1^{st}$ reference voltage to the comparator. Two redundant clock cycles in the typical design could be eliminated in the proposed A/D converter.

  • PDF

TWO-SIDED BEST SIMULTANEOUS APPROXIMATION

  • Rhee, Hyang Joo
    • 충청수학회지
    • /
    • 제23권4호
    • /
    • pp.705-710
    • /
    • 2010
  • Let $C_1(X)$ be a normed linear space over ${\mathbb{R}}^m$, and S be an n-dimensional subspace of $C_1(X)$ with spaned by {$s_1,{\cdots},s_n$}. For each ${\ell}$- tuple vectors F in $C_1(X)$, the two-sided best simultaneous approximation problem is $$\min_{s{\in}S}\;\max\limits_{i=1}^\ell\{{\parallel}f_i-s{\parallel}_1\}$$. A $s{\in}S$ attaining the above minimum is called a two-sided best simultaneous approximation or a Chebyshev center for $F=\{f_1,{\cdots},f_{\ell}\}$ from S. This paper is concerned with algorithm for calculating two-sided best simultaneous approximation, in the case of continuous functions.

실시간 2차원 학습 신경망을 이용한 전기.유압 서보시스템의 추적제어 (Tracking Control of a Electro-hydraulic Servo System Using 2-Dimensional Real-Time Iterative Learning Algorithm)

  • 곽동훈;조규승;정봉호;이진걸
    • 제어로봇시스템학회논문지
    • /
    • 제9권6호
    • /
    • pp.435-441
    • /
    • 2003
  • This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

유전 알고리즘을 이용한 모듈화된 신경망의 비선형 함수 근사화 (Nonlinear Function Approximation of Moduled Neural Network Using Genetic Algorithm)

  • 박현철;김성주;김종수;서재용;전홍태
    • 한국지능시스템학회:학술대회논문집
    • /
    • 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
    • /
    • pp.10-13
    • /
    • 2001
  • Nonlinear Function Approximation of Moduled Neural Network Using Genetic Algorithm Neural Network consists of neuron and synapse. Synapse memorize last pattern and study new pattern. When Neural Network learn new pattern, it tend to forget previously learned pattern. This phenomenon is called to catastrophic inference or catastrophic forgetting. To overcome this phenomenon, Neural Network must be modularized. In this paper, we propose Moduled Neural Network. Modular Neural Network consists of two Neural Network. Each Network individually study different pattern and their outputs is finally summed by net function. Sometimes Neural Network don't find global minimum, but find local minimum. To find global minimum we use Genetic Algorithm.

  • PDF

열탄소성 구성방정식 적분을 위한 새로운 알고리즘 (A New Algorithm for the Integration of Thermal-Elasto-Plastic Constitutive Equation)

  • 이동욱;신효철
    • 대한기계학회논문집
    • /
    • 제18권6호
    • /
    • pp.1455-1464
    • /
    • 1994
  • A new and efficient algorithm for the integration of the thermal-elasto-plastic constitutive equation is proposed. While it falls into the category of the return mapping method, the algorithm adopts the three point approximation of plastic corrector within one time increment step. The results of its application to a von Mises-type thermal-elasto-plastic model with combined hardening and temperature-dependent material properties show that the accurate iso-error maps are obtained for both angular and radial errors. The accuracy achieved is because the predicted stress increment in a single step calculation follows the exact value closely not only at the end of the step but also through the whole path. Also, the comparison of the computational time for the new and other algorithms shows that the new one is very efficient.

퓨리에 급수 근사를 이용한 궤환을 가진 반복 학습제어와 로보트 궤적 추종에의 응용 (Iterative Learning Control with Feedback Using Fourier Series with Application to Robot Trajectory Tracking)

  • 이종운;이학성
    • 전자공학회논문지B
    • /
    • 제30B권4호
    • /
    • pp.67-75
    • /
    • 1993
  • The Fourier series are employed to approximate the input/output(I/O) characteristics of a dynamic system and, based on the approximation, a new learing control algorithm is proposed in order to find iteratively the control input for tracking a desired trajectory. The use of the Fourier approximation of I/O renders at least a couple of useful consequences: the frequency characteristics of the system can be used in the controller design and the reconstruction of the system states is not required. The convergence condition of the proposed algorithm is provided and the existence and uniqueness of the desired control input is discussed. The effectiveness of the proposed algorithm is illustrated by computer simulation for a robot trajectory tracking. It is shown that, by adding feedback term in learning control algorithm, robustness and convergence speed can be improved.

  • PDF

A Novel Subspace Tracking Algorithm and Its Application to Blind Multiuser Detection in Cellular CDMA Systems

  • Ali, Imran;Kim, Doug-Nyun;Song, Yun-Jeong;Azeemi, Naeem Zafar
    • Journal of Communications and Networks
    • /
    • 제12권3호
    • /
    • pp.216-221
    • /
    • 2010
  • In this paper, we propose and develop a new algorithm for the principle subspace tracking by orthonormalizing the eigenvectors using an approximation of Gram-Schmidt procedure. We carry out a novel mathematical derivation to show that when this approximated version of Gram-Schmidt procedure is added to a modified form of projection approximation subspace tracking deflation (PASTd) algorithm, the eigenvectors can be orthonormalized within a linear computational complexity. While the PASTd algorithm tries to extracts orthonormalized eigenvectors, the new scheme orthonormalizes the eigenvectors after their extraction, yielding much more tacking efficiency. We apply the new tracking scheme for blind adaptive multiuser detection for non-stationary cellular CDMA environment and use extensive simulation results to demonstrate the performance improvement of the proposed scheme.

시스템 특성함수 기반 평균보상 TD(${\lambda}$) 학습을 통한 유한용량 Fab 스케줄링 근사화 (Capacitated Fab Scheduling Approximation using Average Reward TD(${\lambda}$) Learning based on System Feature Functions)

  • 최진영
    • 산업경영시스템학회지
    • /
    • 제34권4호
    • /
    • pp.189-196
    • /
    • 2011
  • In this paper, we propose a logical control-based actor-critic algorithm as an efficient approach for the approximation of the capacitated fab scheduling problem. We apply the average reward temporal-difference learning method for estimating the relative value functions of system states, while avoiding deadlock situation by Banker's algorithm. We consider the Intel mini-fab re-entrant line for the evaluation of the suggested algorithm and perform a numerical experiment by generating some sample system configurations randomly. We show that the suggested method has a prominent performance compared to other well-known heuristics.