Finding Fuzzy Rules for IRIS by Neural Network with Weighted Fuzzy Membership Function

Lim, Joon Shik Korean Institute of Intelligent Systems 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.2

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

Finding Optimized NC Drill Paths by Analyzing Gerber CAD Data

Joon Shik Lim,Dae Hoon Hwang 대한전자공학회 1996 ITC-CSCC :International Technical Conference on Ci Vol.- No.-

Finding Fuzzy Rules for IRIS by Neural Network with Weighted Fuzzy Membership Function

Joon Shik Lim 한국지능시스템학회 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.2

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

자동 조기심실수축 탐지를 위한 최소 퍼지소속함수의 추출

임준식 ( Joon-shik Lim ) 한국인터넷정보학회 2007 인터넷정보학회논문지 Vol.8 No.1

본 논문은 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions, NEWFM)을 이용하여 심전도(ECG) 신호로부터 조기심실수축(premature ventricular contractions, PVC)을 자동 탐지하는 방안을 제시하고 있다. NEWFM은 MIT-BIH 데이터베이스의 부정맥 심전도를 웨이블릿 변환(wavelet transform, WT)한 계수로부터 학습하여 정상 파형과 PVC 파형을 구분한다. 비중복면적 분산 측정법을 적용하여 중요도가 가장 높은 웨이블릿 변환의 d3과 d4의 8개 계수를 추출하였다. 이들 특징입력을 3개의 실험군에 사용하여 각각 99.80%, 99.21%, 98.78%의 신뢰성 있는 전체분류율을 나타내었고, 이는 각 실험군에 대한 특징입력의 종속성이 적음을 보여준다. 추출된 8개 계수의 ECG 신호 구간과 퍼지소속함수를 제시함으로써 특징입력에 대한 명시적인 해석을 가능하게 하였다. This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99.80%, 99.21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.

퍼지-기반 낙폭 제한을 적용한 실시간 입찰 에이전트 시스템

임준식 ( Joon-shik Lim ) 한국인터넷정보학회 2001 인터넷정보학회논문지 Vol.2 No.2

본 논문에서는 에이전트를 이용하여 판매자와 구매자가 실시간으로 인터넷을 통하여 효율적인 입찰을 할 수 있는 희망구매가 근접 방식의 입찰 시스템을 구현하고 있다. 특히 판매자들이 제시하는 가격의 낙폭 제한선을 적절하게 조절해 줌으로써 구매자의 희망 구매가에 근접한 낙찰을 유도하고, 유찰율을 줄일 수 있는 퍼지-기반 낙폭 제한 추론 방안을 제안하고 있다. This paper implements the desirable-bid price type bidding agent system that enables internet bidding between buyer and sellers efficiently in real Time. The agent system controls the bid-limit to approach successful desirable-bid price of a buyer, to reduce unsuccessful bid rate using fuzzy-based bid-limit inference method.

지능형 최단 경로 , 최소 꺾임 경로 및 혼합형 최단 경로 찾기

임준식(Lim Joon Shik) 한국정보처리학회 1996 정보처리학회논문지 Vol.3 No.1

This paper presents new heuristic search algorithms for searching rectilinear(L1), link metric, and combined shortest paths in the presence of orthogonal obstacles. The GMD(Guided Minimum Detour) algorithm combines the best features of maze-running algorithms and line-search algorithms. The LGMD(Line-by-Line Guided Minimum Detour) algorithm is a modification of the GMD algorithm that improves efficiency using line-by-line extensions. Our GMD and LGMD algorithms always find a rectilinear shortest path using the guided A* search method without constructing a connection graph that contains a shortest path. The GMD and the LGMD algorithms can be implemented in O(m - e log e N log N)and O(e log e N log N) time, respectively, and O(e N) space, where m is the total number of searched nodes, e is the number of boundary sides of obstacles, and N is the total number of searched line segments. Based on the LGMD algorithm, we consider not only the problems of finding a link metric shortest path is terms of the number of bends, but also the combined L1 metric and link metric shortest path in terms of the length and the number of bends.

NEWFM을 이용한 자동 조기심실수축 탐지

임준식(Joon Shik Lim) 한국지능시스템학회 2006 한국지능시스템학회논문지 Vol.16 No.3

본 논문은 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions, NEWFM)을 이용하여 심전도(ECG) 신호로부터 조기심실수축(premature ventricular contractions, PVC)을 자동 탐지하는 방안을 제시하고 있다. NEWFM은 MIT-BIH 데이터베이스의 부정맥 심전도를 웨이블릿 변환(wavelet transform, WT)한 계수로부터 학습하여 정상 파형과 PVC 파형을 구분한다. 비중복면적 분산 측정법을 적용하여 중요도가 가장 높은 계수 2개를 추출하여 분류규칙을 최소화하였고, 이를 사용하여 99.90%의 PVC 분류성능을 나타내었다. 또한 추출된 두 계수의 R파를 기준으로 한 위치를 제시함으로써 두 위치의 정보만으로 PVC를 탐지할 수 있음을 보여주었다. This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

학술전문가 선정을 위한 지식 기반 언어적 접근

임준식(Joon Shik Lim) 한국지능시스템학회 2002 한국지능시스템학회논문지 Vol.12 No.6

퍼지신경망과 비중복면적 분산 측정법을 이용한 최소의 특징입력 및 퍼지규칙의 추출

임준식(Joon Shik Lim) 한국지능시스템학회 2005 한국지능시스템학회논문지 Vol.15 No.5

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NEWFM)을 이용하여 위스콘신 유방암(Wisconsin breast cancer)의 진단을 수행하는 퍼지규칙을 추출하고 비중복면적 분산 측정법을 사용하여 특정입력수를 최소로하는 방안을 제안하고 있다. NEWFM 구조의 중간 부분인 하이퍼박스(hyperbox)들은 n 개의 대, 중, 소로 구성된 가중 퍼지소속함수 집합으로 구성되며, 학습 후 각 집합의 대, 중, 소로 구성된 가중 퍼지소속함수는 퍼지집합의 경계합(bounded sum)을 사용하여 다시 하나의 가중 퍼지소속함수로 합성(BSWFM) 된다. n 개의 특정입력 (feature input)은 학습된 모든 하이퍼박스에 연결되어 예측 작업을 수행한다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 특정입력을 제거하면서 최소의 m 개 특정업력만을 사용한 하이퍼박스로 단순화 시킨다. 이러한 방법으로 위스콘신 유방암의 9개의 특징입력 중 4개를 사용하여 NEWFM으로 추출된 2개의 퍼지규칙은 99.71%의 예측 인식율을 가지며 이는 퍼지규칙의 수와 인식율에 있어 현재 발표된 논문의 결과보다 우수함을 보여준다. This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature input using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

Parameter Estimation Method of Low-Frequency Oscillating Signals Using Discrete Fourier Transforms

Joon-Ho Choi,Kwan-Shik Shim,Hae-Kon Nam,Young-Chul Lim,Soon-Ryul Nam 대한전기학회 2012 Journal of Electrical Engineering & Technology Vol.7 No.2

This paper presents a DFT (Discrete Fourier Transform) based estimation algorithm for the parameters of a low-frequency oscillating signal. The proposed method estimates the parameters, i.e., the frequency, the damping factor, the mode amplitude, and the phase, by fitting a discrete Fourier spectrum with an exponentially damped cosine function. Parameter estimation algorithms that consider the spectrum leakage of the discrete Fourier spectrum are introduced. The multi-domain mode test functions are tested in order to verify the accuracy and efficiency of the proposed method. The results show that the proposed algorithms are highly applicable to the practical computation of low-frequency parameter estimations based on DFTs.