• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.140-145
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• 2008

Fuzzy partition is a conceptual vehicle that encapsulates data into information granules. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. A few algorithms generating fuzzy partitions with fuzzy equalization have been suggested. Simulations and experiments have showed that fuzzy partition representing more characteristics of given input distribution usually produces meaningful results. In this paper, given two points and cardinality of fuzzy partition, we prove that it is not true that there always exists a fuzzy partition with fuzzy equalization in which two of points having peaks fall on the given two points. Then, we establish an algorithm that minimizes the maximum distance between given two points and adjacent points having peaks in the partition. A numerical example is presented to show the validity of the suggested algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.12-17
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• 2013

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.571-578
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• 2002

In temporal database, extended time dimension for history management brings about complexity of join operation and increased cost. To solve this problem, a method that joins the divided segment time data after partition the time range into fixed time interval is introduced. But existing methods can't solve the ambiguity problem of time border that caused by temporal granularity in the partition point. In this paper, We suggested Fuzzy Minimum Interval Partition (FMIP) method that introduced the possibility distribution of fuzzy theory considered uncertainty time interval border in the partition line.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.30-33
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• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2157-2164
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• 2011

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2072-2078
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• 2011

In photovoltaic(PV) system, for obtaining maximum efficiency of solar power systems, the solar tracking system must be controlled to match position of the sun. In this paper, we design the solar tracking system to track movement of the sun using CdS sensor modules and to determine direction of the sun under shadow of directions. In addition, for an intelligent computation in tracking of the sun, a fuzzy controller is allocated to space avaliable for splitting area of fuzzy part for the fuzzy input space(grid-type fuzzy partition) in which a fuzzy grid partition divides fuzzy rules bases. As well, a simple model of solar tracking system is designed by two-axis motor control systems and the 8-direction sensor module that can measure shadow from CdS sensor modules by matching of axis of CdS modules and PV panels. We demonstrate this systems is effective for fixed location and moving vessels and our fuzzy controller can track the satisfactorily.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.40-47
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• 2009

Recently, dynamic proxy caching has been proposed on the distributed environment so that media objects by user's requests can be served directly from the proxy without contacting the server. However, it makes caching challenging due to multimedia large sizes, low latency and continuous streaming demands of media objects. To solve the problems caused by streaming demands of media objects, this paper has been proposed the grouping scheme with fuzzy filtering based on partition and mapping. For partition and mapping, this paper divides media block segments into fixed partition reference block(R$_f$P) and variable partition reference block(R$_v$P). For semantic relationship, it makes fuzzy relationship to performs according to the fixed partition temporal synchronization(T$_f$) and variable partition temporal synchronization(T$_v$). Simulation results show that the proposed scheme makes streaming service efficiently with a high average request response time rate and cache hit rate and with a low delayed startup ratio compared with other schemes.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.5 s.98
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• pp.48-57
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• 1999

Applications of thresholding technique are based on the assumption that object and background pixels in a digital image can be distinguished by their gray level values. For the segmentation of more complex images, it is necessary to resort to multiple threshold selection techniques. This paper describes a new method for multiple threshold selection of gray level images which are not clearly distinguishable from the background. The proposed method consists of three main stages. In the first stage, a probability distribution function for a gray level histogram of an image is derived. Cluster points are defined according to the probability distribution function. In the second stage, fuzzy partition matrix of the probability distribution function is generated through the fuzzy clustering process. Finally, elements of the fuzzy partition matrix are classified as clusters according to gray level values by using max-membership method. Boundary values of classified clusters are selected as multiple threshold. In order to verify the performance of the developed algorithm, automatic inspection process of ball grid array is presented.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.494-498
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• 1996

Three fuzzy input space partitoining methods, which are grid, tree, and scatter method, are mainly used until now. These partition methods represent good performance in the modeling of the linear system and nonlinear system with independent modeling variables. But in the case of the nonlinear system with the coupled modeling variables, there should be many fuzzy rules for acquiring the exact fuzzy model. In this paper, it shows that the fuzzy model is acquired using transformed modeling vector by linear transformation of the modeling vector.