• Title/Summary/Keyword: generalized eigenvalue problem (GEVP)

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Synthesis of GBSB Neural Associative Memories Using GEVP (GEVP를 이용한 GBSB 연상 메모리의 설계)

  • Park, Yon-Mook;Park, Joo-Young
    • Proceedings of the KIEE Conference
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    • 1999.07g
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    • pp.2872-2875
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    • 1999
  • 본 논문은 주어진 적합한 이진 패턴들의 집합이 점근적으로 안정한 평형점들로써 저장되는 최적으로 성능을 갖는 GBSB (generalized brain-state-in-a-box)의 설계가 고려된다. GBSB 모델의 정성적 특성에 기초하여, 설계 문제가 제약 조건을 가한 최적화 문제로 공식화된다. 다음으로, 우리는 이 문제를 GEVP (generalized eigenvalue Problem)라고 불리는 최적화 문제로 변환한다. 제안된 방법을 예증하기 위함과 기존의 방법과의 비교를 위해서 설계 예제가 주어진다.

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Implementation of Bidirectional Associative Memories Using the GBAM Model with Bias Terms (바이어스항이 있는 GBAM 모델을 이용한 양방향 연상메모리 구현)

  • 임채환;박주영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2001.05a
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    • pp.69-72
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    • 2001
  • In this paper, we propose a new design method for bidirectional associative memories model with high error correction ratio. We extend the conventional GBAM model using bias terms and formulate a design procedure in the form of a constrained optimization problem. The constrained optimization problem is then transformed into a GEVP(generalized eigenvalue problem), which can be efficiently solved by recently developed interior point methods. The effectiveness of the proposed approach is illustrated by a example.

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A New Design Method for the GBAM (General Bidirectional Associative Memory) Model (GBAM 모델을 위한 새로운 설계방법)

  • 박주영;임채환;김혜연
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.4
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    • pp.340-346
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    • 2001
  • This paper proposes a new design method for the GBAM: (general bidirectional associative memory) model. Based on theoretical investigations on the GBAM: model, it is shown that the design of the GBAM:-based bidirectional associative memeories can be formulated as optimization problems called GEVPs (generalized eigenvalue problems). Since the GEVPs arising in the procedure can be efficiently solved within a given tolerance by the recently developed interior point methods, the design procedure established in this paper is very useful in practice. The applicability of the proposed design procedure is demonstrated by simple design examples considered in related studies.

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Design of BAM using an Optimization approach (최적화기법을 이용한 BAM의 설계)

  • 권철희
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.2
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    • pp.161-167
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    • 2000
  • In this paper, we propose a design method for BAMs(bidirectiona1 associative memories) which can perform the function of bidirectional association efficiently. Based on the theoretical investigation about the properties of BAMs, we first formulate the problem of finding a BAM that can store the given pattern pairs as stable states with high error correction ratio in the form of a constrained optimization problem. Next, we transform the constrained optimization problem into a GEVP(genera1ized eigenvalue problem), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

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A Study on Short-Term Load Forecasting System Using Data Mining (데이터 마이닝을 이용한 단기부하예측 시스템 연구)

  • Kim, Do-Wan;Park, Jin-Bae;Kim, Juhg-Chan;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.588-591
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    • 2003
  • This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

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Stability Bounds of Time-Varying Uncertainty and Delay Time for Discrete Systems with Time-Varying Delayed State (시변 시간지연을 갖는 이산시스템의 시변 불확실성의 안정 범위)

  • Han, Hyung-Seok
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.10
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    • pp.895-901
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    • 2012
  • The stability robustness problem of linear discrete systems with time-varying unstructured uncertainty of delayed states with time-varying delay time is considered. The proposed conditions for stability can be used for finding allowable bounds of timevarying uncertainty and delay time, which are solved by using LMI (Linear Matrix Inequality) and GEVP (Generalized Eigenvalue Problem) known as powerful computational methods. Furthermore, the conditions can imply the several previous results on the uncertainty bounds of time-invariant delayed states. Numerical examples are given to show the effectiveness of the proposed algorithms.

A Design Methodology for CNN-based Associative Memories (연상 메모리 기능을 수행하는 셀룰라 신경망의 설계 방법론)

  • Park, Yon-Mook;Kim, Hye-Yeon;Park, Joo-Young;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.27 no.5
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    • pp.463-472
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    • 2000
  • In this paper, we consider the problem of realizing associative memories via cellular neural network(CNN). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNN that can store given binary vectors with optimal performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities(LMIs). Finally, we reformulate the synthesis problem as a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. Proposed method can be applied to both space varying template CNNs and space-invariant template CNNs. The validity of the proposed approach is illustrated by design examples.

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