• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.129-135
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• 2008

This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• Journal of the Korean Society of Safety
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• v.20 no.1 s.69
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• pp.126-132
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• 2005

The purpose of this study is to propose a method to evaluate task complexity using CIFs(Complexity Influencing Factors). We developed a method that CIFs can be used in the evaluation of task complexity using fuzzy linguistic approach. That is, a fuzzy linguistic multi-criteria method to assess task complexity in a specific task situation was proposed. The CIFs luting was assessed in linguistic terms, which are described by fuzzy numbers with triangular and trapezoidal membership function. A fuzzy weighted average algorithm, based on the extension principle, was employed to aggregate these fuzzy numbers. Finally, the method was validated by experimental approach. In the result, it was validated that TCIM(Tink Complexity Index Method) is an efficient method to evaluate task complexity because the correlation coefficient between task performance time and TCI(Task Complexity Index) was 0.699.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.109-116
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• 2014

This study proposed new instance selection using neural network with weighted fuzzy membership functions(NEWFM) based on Takagi-Sugeno(T-S) fuzzy model to improve the classification performance. The proposed instance selection adopted weighted average defuzzification of the T-S fuzzy model and an interval selection, same as the confidence interval in a normal distribution used in statistics. In order to evaluate the classification performance of the proposed instance selection, the results were compared with depending on whether to use instance selection from the case study. The classification performances of depending on whether to use instance selection show 77.33% and 78.19%, respectively. Also, to show the difference between the classification performance of depending on whether to use instance selection, a statistics methodology, McNemar test, was used. The test results showed that the instance selection was superior to no instance selection as the significance level was lower than 0.05.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.502-506
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• 2013

The cash flow forecasting is normally done by contractors in construction industry at early stages of the project for contractual decisions. The decision making in such situations involve uncertainty about future cash flows and assessment of working capital requirements gains more importance in projects constrained by cash. The traditional approach to assess the working capital requirements is deterministic in and neglects the uncertainty. This paper presents an alternate approach to assessment of working capital requirements for contractor based on fuzzy set theory by considering the uncertainty and ambiguity involved at payment periods. Statistical methods are used to deal with the uncertainty for working capital curves. Membership functions of the fuzzy sets are developed based on these statistical measures. Advantage of fuzzy peak working capital requirements is demonstrated using peak working capital requirements curves. Fuzzy peak working capital requirements curves are compared with deterministic curves and the results are analyzed. Fuzzy weighted average methodology is proposed for the assessment of peak working capital requirements.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.359-365
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• 2012

The fuzzy set framework has a number of properties that make it suitable to formulize uncertain information in medical diagnosis. This study introduces a fuzzy diagnostic method based on the interval-valued interview chart and the interval-valued intuitionistic fuzzy weighted arithmetic average(IIFWAA) operator. An issue in the use of the IIFWAA operator is to determine the weights. In this study, we propose the occurrence information of symptoms as the weights. An illustrative example is provided to demonstrate its practicality and effectiveness.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.6
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• pp.759-766
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• 2003

The amount of container freight continuously has been increased. and the low efficiency of container crane causes jamming frequently in transportation and cargo handling at port. The conventional control techniques based on a mathematical model are not well suited for dealing with ill-defined and uncertain systems. Recently. Fuzzy control has been successfully applied to a wide variety of practical problems as robots. automatic train operation system. etc. In this paper. a fuzzy controller for container crane is proposed to accomplish a design of improved control system for minimizing the swing motion at destination. In this scheme a mathematical model for the system is obtained in state space form. Finally. to exhibit the tracking performance and robustness of the proposed controller. computer simulations were carried out with various references, parameter variations and disturbances.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.206-211
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• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1406-1409
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• 1993

This paper describes a model of the subjective reliability analysis, which uses a fuzzy set, natural language expressions and parameterized operations of fuzzy sets, and reflects analysts' subjectivity. The model has the problem of many different analysis results being obtained since the results depend on their subjectivity. As one of the solutions two kinds of mutual agreements based on the analysis results are considered. One is the intersection and the union of the fuzzy sets obtained by the analysis. The other is the weighted average of the fuzzy sets. This paper gives these interpretations from the viewpoint of system reliability analysis. This paper also shows examples of these considerations.