• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.194-202
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• 2000

In this paper, an efficient fuzzy rule generation scheme for Adaptive Network-based Fuzzy Inference System(ANFIS) using the conditional fuzzy-means(CFCM) and fuzzy equalization(FE) methods is proposed. Usually, the number of fuzzy rules exponentially increases by applying the gird partitioning of the input space, in conventional ANFIS approaches. Therefore, CFCM method is adopted to render the clusters which represent the given input and output fuzzy and FE method is used to automatically construct the fuzzy membership functions. From this, one can systematically obtain a small size of fuzzy rules which shows satisfying performance for the given problems. Finally, we applied the proposed method to the truck backer-upper control and Box-Jenkins modeling problems and obtained a better performance than previous works.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1541-1544
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• 2003

In This Paper, alternative method fur data pattern estimation is proposed and its numerical experiment is carried out. Proposed method gives candidates cluster numbers of given data set between n-2 and 2 by means of movement of the center of gravity. To observe the performance of proposed method, Test sample data sets are offered. Finally, this method is applied to Box and Jenkins's gas furnace data to verify the performance with previous researches.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.138-143
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• 2006

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3019-3021
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• 2000

In this paper. a new fuzzy modeling algorithm is proposed : it can express a given unknown system with a small number of fuzzy rules and be easily implemented. This method uses an independent array instead of a lattice form for a premise membership function. For the purpose of getting the initial value of fuzzy rules. the method uses the fuzzy c-means clustering method. To optimally tune the initial fuzzy rule. the DNA coding method is also utilized at same time. Box and Jenkins's gas furnace data is used to illustrate the validity of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.417-422
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• 2000

In this paper, an efficient fuzzy rule generation scheme for adaptive neuro-fuzzy system modeling using the Independent Component Analysis(ICA) as a preprocessing is proposed. Correlation between inputs was not considered in the conventional neuro- fuzzy modeling schemes, such that enormous number of rules and large amount of error were unavoidable. The correlation between inputs is weakened by employing ICA so that the number of rules and the amount of error are reduced. In simulation, the Box-Jenkins furnace data is used to verify the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2490-2495
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• 2005

The scheduling of plant should be determined based on the product demands correctly forecasted by reasonable methods. However, because most existing forecasting packages need user's knowledge about forecasting, it has been hard for plant engineers without forecasting knowledge to apply forecasted demands to scheduling. Therefore, a forecasting module has been developed for plant engineers without forecasting knowledge. In this study, for the development of the forecasting module, an automatic method using the ARIMA model that is framed from the modified Box-Jenkins process is proposed. And a new method for safety inventory determination is proposed to reduce the penalty cost by forecasting errors. Finally, using the two proposed methods, the web-based automatic module has been developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.57-65
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• 2011

Monitoring auto correlated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment through sensor rather than resultant output status of the processes. Equipment's sensor data show various forms of correlation features. Among them, considerable amount of sensor data, statistically autocorrelated, is well represented by Box-Jenkins autoregressive moving average (ARMA) model. In this paper, we present a design method of statistical process control (SPC) used for monitoring processes represented by the ARMA model. The proposed method shows benefits in the power of detecting process changes, and considers robustness to ARMA modeling errors simultaneously. We prove benefits through Monte carlo simulation-based investigations.