• Title/Summary/Keyword: clustering modeling

Search Result 256, Processing Time 0.024 seconds

Neuro-Fuzzy Modeling based on Self-Organizing Clustering (자기구성 클러스터링 기반 뉴로-퍼지 모델링)

  • Kim Sung-Suk;Ryu Jeong-Woong;Kim Yong-Tae
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.15 no.6
    • /
    • pp.688-694
    • /
    • 2005
  • In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

Toward precise and accurate modeling of matter clustering in redshift space

  • Oh, Minji
    • The Bulletin of The Korean Astronomical Society
    • /
    • v.43 no.2
    • /
    • pp.40.3-40.3
    • /
    • 2018
  • This dissertation presents the results on two-dimensional Redshift space distortion (hereafter RSD) analyses of the large-scale structure of the universe using spectroscopic data and on improvement of modeling of the RSD effect. RSD is an effect caused by galaxies' peculiar velocity on their clustering feature in observation along the line of sight and is thus intimately connected to the growth rate of the structure in the universe, from which we can test the origin of cosmic acceleration and Einstein's theory of gravity at cosmic scales in the end. However, there are several challenges in modeling precise and accurate RSD effect, such as non-linearities and the existence of an exotic component, e.g. massive neutrino. As part of endeavors for modeling more precise and accurate galaxy clustering in redshift space, this dissertation includes a series of works for this issue. (More detailed descriptions were omitted.)

  • PDF

A Neuro-Fuzzy Modeling using the Hierarchical Clustering and Gaussian Mixture Model (계층적 클러스터링과 Gaussian Mixture Model을 이용한 뉴로-퍼지 모델링)

  • Kim, Sung-Suk;Kwak, Keun-Chang;Ryu, Jeong-Woong;Chun, Myung-Geun
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.13 no.5
    • /
    • pp.512-519
    • /
    • 2003
  • In this paper, we propose a neuro-fuzzy modeling to improve the performance using the hierarchical clustering and Gaussian Mixture Model(GMM). The hierarchical clustering algorithm has a property of producing unique parameters for the given data because it does not use the object function to perform the clustering. After optimizing the obtained parameters using the GMM, we apply them as initial parameters for Adaptive Network-based Fuzzy Inference System. Here, the number of fuzzy rules becomes to the cluster numbers. From this, we can improve the performance index and reduce the number of rules simultaneously. The proposed method is verified by applying to a neuro-fuzzy modeling for Box-Jenkins s gas furnace data and Sugeno's nonlinear system, which yields better results than previous oiles.

Advance Neuro-Fuzzy Modeling Using a New Clustering Algorithm (새로운 클러스터링 알고리듬을 적용한 향상된 뉴로-퍼지 모델링)

  • 김승석;김성수;유정웅
    • The Transactions of the Korean Institute of Electrical Engineers D
    • /
    • v.53 no.7
    • /
    • pp.536-543
    • /
    • 2004
  • In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

A novel Neuro Fuzzy Modeling using Gaussian Mixture Models

  • Kim, Sung-Suk;Kwak, Keun-Chang;Kim, Sung-Soo;Chun, Myung-Geun;Ryu, Jeong-Woong
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 2002.10a
    • /
    • pp.110.1-110
    • /
    • 2002
  • We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

  • PDF

Neural-based Blind Modeling of Mini-mill ASC Crown

  • Lee, Gang-Hwa;Lee, Dong-Il;Lee, Seung-Joon;Lee, Suk-Gyu;Kim, Shin-Il;Park, Hae-Doo;Park, Seung-Gap
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.12 no.6
    • /
    • pp.577-582
    • /
    • 2002
  • Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

Fuzzy Controller Modeling for Electromagnetic Levitation Systems based on Clustering Algorithm (클러스터링에 기초한 자기부상시스템의 퍼지제어기 모델링)

  • Kim, Min-Soo;Byun, Yeun-Sub;Lee, Kwan-Sup
    • Proceedings of the KSR Conference
    • /
    • 2006.11a
    • /
    • pp.145-159
    • /
    • 2006
  • This paper describes the development of a clustering based fuzzy controller of an electromagnetic suspension vehicle using gain scheduling method and Kalman filter for a simplified single magnet system. Electromagnetic suspension vehicle systems are highly nonlinear and essentially unstable systems For achieving the levitation control of the DC electromagnetic suspension system, we considered a fuzzy system modeling method based on clustering algorithm which a set of input/output data is collected from the well defined Linear Quadratic Gaussian(LQG) controller. Simulation results show that the proposed clustering based fuzzy controller methodology robustly yields uniform performance with adequate gap response over the mass variation range.

  • PDF

Effective Acoustic Model Clustering via Decision Tree with Supervised Decision Tree Learning

  • Park, Jun-Ho;Ko, Han-Seok
    • Speech Sciences
    • /
    • v.10 no.1
    • /
    • pp.71-84
    • /
    • 2003
  • In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

  • PDF

Extreme value modeling of structural load effects with non-identical distribution using clustering

  • Zhou, Junyong;Ruan, Xin;Shi, Xuefei;Pan, Chudong
    • Structural Engineering and Mechanics
    • /
    • v.74 no.1
    • /
    • pp.55-67
    • /
    • 2020
  • The common practice to predict the characteristic structural load effects (LEs) in long reference periods is to employ the extreme value theory (EVT) for building limit distributions. However, most applications ignore that LEs are driven by multiple loading events and thus do not have the identical distribution, a prerequisite for EVT. In this study, we propose the composite extreme value modeling approach using clustering to (a) cluster initial blended samples into finite identical distributed subsamples using the finite mixture model, expectation-maximization algorithm, and the Akaike information criterion; (b) combine limit distributions of subsamples into a composite prediction equation using the generalized Pareto distribution based on a joint threshold. The proposed approach was validated both through numerical examples with known solutions and engineering applications of bridge traffic LEs on a long-span bridge. The results indicate that a joint threshold largely benefits the composite extreme value modeling, many appropriate tail approaching models can be used, and the equation form is simply the sum of the weighted models. In numerical examples, the proposed approach using clustering generated accurate extrema prediction of any reference period compared with the known solutions, whereas the common practice of employing EVT without clustering on the mixture data showed large deviations. Real-world bridge traffic LEs are driven by multi-events and present multipeak distributions, and the proposed approach is more capable of capturing the tendency of tailed LEs than the conventional approach. The proposed approach is expected to have wide applications to general problems such as samples that are driven by multiple events and that do not have the identical distribution.

Design of Granular-based Neurocomputing Networks for Modeling of Linear-Type Superconducting Power Supply (리니어형 초전도 전원장치 모델링을 위한 입자화 기반 Neurocomputing 네트워크 설계)

  • Park, Ho-Sung;Chung, Yoon-Do;Kim, Hyun-Ki;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.59 no.7
    • /
    • pp.1320-1326
    • /
    • 2010
  • In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.