• 전자공학회논문지B
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• 제31B권8호
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.1176-1181
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• 2006

본 연구에서는 병렬구조를 가진 안동댐과 임하댐에 대하여 할당법칙(Allocation rule, AR)을 이용하여 용수공급해석과 신뢰도지표를 산정하였다. 할당계수의 분석결과로는 안동댐이 용수공급의 기여도가 임하댐과 비교해서 Rule(A), Rule(B)에서는 66%이상으로 분석되었으며, Rule(C)의 경우 거의 대등한 기여를 하는 것으로 분석되었다. Rule(C)의 경우는 안동댐과 임하댐의 각각의 저류상태와 공급능력 상태에 따라 저류량과 유입량의 합을 댐의 평균저류량으로 나눈 비에 의해 공급량이 할당되어서 우수한 결과를 나타내었다. 병렬저수지 시스템의 용수공급의 분석기준에 있어 월별로 일정한 양을 기준으로 분석하는 것보다는 월별 용수변화량을 고려한 경우가 용수공급분석에 있어 우수한 결과를 나타나는 것으로 분석되었다. 본 연구를 기초로 하여 용수공급해석을 위한 새로운 기법이 개발되어 병렬저수지에 적용되었으며, 이 운영규칙은 여러 종류의 병렬저수지 시스템의 적용에 있어 효율적인 운영방안을 수립할 수 있을 것이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• ETRI Journal
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• 제34권4호
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• pp.629-632
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• 2012

This letter investigates the combination of the Chase-2 and sum-product (SP) algorithms for low-density parity-check (LDPC) codes. A simple modification of the tanh rule for check node update is given, which incorporates test error patterns (TEPs) used in the Chase algorithm into SP decoding of LDPC codes. Moreover, a simple yet effective approach is proposed to construct TEPs for dealing with decoding failures with low-weight syndromes. Simulation results show that the proposed algorithm is effective in improving both the waterfall and error floor performance of LDPC codes.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.37-45
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• 2000

Consider the problem of estimating a $p{\times}1$ mean vector ${\underline{\theta}}(p{\geq}4)$ under the quadratic loss, based on a sample ${\underline{x}_{1}},\;{\cdots}{\underline{x}_{n}}$. We find an optimal decision rule within the class of Lindley type decision rules which shrink the usual one toward the mean of observations when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}\;{\underline{\theta}}\;-\;{\bar{\theta}}{\underline{1}}\;{\parallel}$ is known, where ${\bar{\theta}}=(1/p){\sum_{i=1}^p}{\theta}_i$ and $\underline{1}$ is the column vector of ones.

• 전자공학회논문지B
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• 제31B권12호
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• pp.116-123
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• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• 조명전기설비학회논문지
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• 제16권5호
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• pp.104-111
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• 2002

본 논문에서는 EM(Expectation-Maximization) 알고리즘을 이용한 자동적인 퍼지 규칙생성과 적응 뉴로-퍼지 제어기(Adaptive Neuro-Fuzzy Controller)의 설계를 제안한다. EM 알고리즘은 가우시안 혼합모델(Gaussian Mixture Model)의 최대우도추정(Maximum Likelihood Estimate)을 위해 사용되어지며 본 논문에서는 규칙생성을 위해 클러스터 중심을 추정한다. 추정된 클러스터는 ANFIS(Adaptive Neuro-Fuzzy Inference System)의 퍼지 규칙과 소속함수를 구축하는데 사용되어진다. 시뮬레이션으로 제안된 적응 뉴로-퍼지 제어기의 성능을 입증하기 위해 목욕물 온도 제어 시스템에 대해 다루고 기존 퍼지 제어기에 비해 적은 규칙의 수와 작은 값의 SAE(Sum of Absolute Error)으로 성능개선을 확인하였다.

• 대한토목학회논문집
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• 제10권3호
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• pp.1-6
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• 1990

다지점지진하중(多支點地震荷重)을 받는 사경문(四徑問) 연속철도교양(連續鐵道橋梁)의 동적거동(動的擧動)을 응답(應答)스펙트럼법(法)에 의하여 조사(調査)하였다. 소진복(小振福)의 진동(振動)과 재료(材料)의 선형(線形)-탄성거동(彈性擧動)을 가정(假定)하였으며, 지반(地盤)-구조물(構造物) 상호작용(相互作用) 효과(效果)는 무시(無視)하였고, 교량(橋梁)의 횡방향(橫方向) 등답(應答)만을 고려(考慮)하였다. 응답(應答)스펙트럼해석(解析)의 결과(結果)는 시간이력해석결과(時間履歷解析結果)와 비교(比較)되었으며 모우드 최대치(最大値)와 지지점운동(支持點運動)의 중량(重量)은 여러가지 조합법칙(組合法則) 즉(卽), Square-Root-Of-Sum-Squares, Double Sum, 그리고 P-Norm Rule을 적용(適用)하였다.

• 경영과학
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• 제13권2호
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• pp.25-48
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• 1996

Mixed Model Assembly Lines (MMALs) are increasingly used to produce differentiated products on a single assembly line without work-in-process storage, Usually, a typical MMAL consists of a number of (1) stations doing exactly the same operation on every job, (2) stations involving operations with different choices, and (3) stations offering operations that are not performed on every job, or that are performed on every job but with many options. For stations of the first type there is no sequencing problem at all. However, for the second type a set-up cost is incurred each time the operation switches from one choice to another. At the third type of stations, different models, requring different amounts and choices of assembly work, creates an uneven flow of work along the line and variations in the work load at these stations. When a subsequence of jobs requires more work load than the station can handle, it is necessary to help the operations at the station or to complete the work elsewhere. Therefore, a schedule which minimize the sum of set-up cost and utility work cost is desired. So this study has developed Fixed Random Ordering Rule (FROR), Fixed Ascending Ordering Rule (FAOR), Fixed Descending Ordering Rule, and Extended NHR (ENHR). ENHR is to choose optimal color ordering of each batch with NHR, and to decide job sequence of the batch with it, too. As the result of experiments, ENHR was the best heuristic algorithm. NHR is a new heuristic rule in which only the minimum addition of violations from both partial sequence and unassigned sequence at every branch could be considered. And this is a heuristic sequencing rule for the third type of stations at MMAL. This study developed one more heuristic algorithm to test the performance of NHR, which is named as Practical Heuristic Rule (PHR).

• Journal of electromagnetic engineering and science
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• 제11권2호
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• pp.71-75
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• 2011

The sum-rate maximization rule can find an optimal user set that maximizes the sum capacity in multiple input multiple output (MIMO) broadcast channels (BCs), but the search space for finding the optimal user set becomes prohibitively large as the number of users increases. The proposed algorithm selects a user set of the largest effective channel norms based on statistical channel state information (CSI) for reducing the computational complexity, and uses Tomlinson-Harashima precoding (THP) for minimizing the interference between selected users in time-varying MIMO BCs.