• Title/Summary/Keyword: Gaussian fuzzy number

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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
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    • 2002.10a
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    • pp.110.1-110
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    • 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...

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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
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    • v.13 no.5
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    • pp.512-519
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    • 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.

Robust Fuzzy Varying Coefficient Regression Analysis with Crisp Inputs and Gaussian Fuzzy Output

  • Yang, Zhihui;Yin, Yunqiang;Chen, Yizeng
    • Journal of Computing Science and Engineering
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    • v.7 no.4
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    • pp.263-271
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    • 2013
  • This study presents a fuzzy varying coefficient regression model after deleting the outliers to improve the feasibility and effectiveness of the fuzzy regression model. The objective of our methodology is to allow the fuzzy regression coefficients to vary with a covariate, and simultaneously avoid the impact of data contaminated by outliers. In this paper, fuzzy regression coefficients are represented by Gaussian fuzzy numbers. We also formulate suitable goodness of fit to evaluate the performance of the proposed methodology. An example is given to demonstrate the effectiveness of our methodology.

A Fuzzy Rule Extraction by EM Algorithm and A Design of Temperature Control System (EM 알고리즘에 의한 퍼지 규칙생성과 온도 제어 시스템의 설계)

  • 오범진;곽근창;유정웅
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.16 no.5
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    • pp.104-111
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    • 2002
  • This paper presents a fuzzy rule extraction method using EM(Expectation-Maximization) algorithm and a design method of adaptive neuro-fuzzy control. EM algorithm is used to estimate a maximum likelihood of a GMM(Gaussian Mixture Model) and cluster centers. The estimated clusters is used to automatically construct the fuzzy rules and membership functions for ANFIS(Adaptive Neuro-Fuzzy Inference System). Finally, we applied the proposed method to the water temperature control system and obtained better results with respect to the number of rules and SAE(Sum of Absolute Error) than previous techniques such as conventional fuzzy controller.

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
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    • v.53 no.7
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    • pp.536-543
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    • 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 Study on the Soiution of Inverse Kinematic of Manipulator using Self-Organizing Neural Network and Fuzzy Compensator (퍼지 보상기와 자기구성 신경회로망을 이용한 매니퓰레이터의 역기구학 해에 관한 연구)

  • 김동희;이수흠;신위재
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.3
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    • pp.79-85
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    • 2001
  • We obtain a solution of inverse kinematic of 3 axis manipulator by using a self-organizing neral network(SONN) with a fuzzy compensator. The self-organizing neural network using the gaussian potential function as the activation function has one hidden layer in the first learning time. The network obtains the optimal number of node by increasing the number of hidden layer node through the learning, and the fuzzy compensator has the optimal loaming rate of neutral network. In this results, we can confirmed that the learning rate is improved and the rapid convergence to the steady-state.

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Optimal Design of Fuzzy Hybrid Multilayer Perceptron Structure (퍼지 하이브리드 다층 퍼셉트론구조의 최적설계)

  • Kim, Dong-Won;Park, Byoung-Jun;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2977-2979
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    • 2000
  • A Fuzzy Hybrid-Multilayer Perceptron (FH-MLP) Structure is proposed in this paper. proposed FH-MLP is not a fixed architecture. that is to say. the number of layers and the number of nodes in each layer of FH-MLP can be generated to adapt to the changing environment. FH-MLP consists of two parts. one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules. and its fuzzy system operates with Gaussian or Triangular membership functions in premise part and constants or regression polynomial equation in consequence part. the other is polynomial nodes which several types of high-order polynomial such as linear. quadratic. and cubic form are used and is connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method. time series data for gas furnace process has been applied.

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Fuzzy-ART Basis Equalizer for Satellite Nonlinear Channel

  • Lee, Jung-Sik;Hwang, Jae-Jeong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.1
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    • pp.43-48
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    • 2002
  • This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

Speaker Identification Using PCA Fuzzy Mixture Model (PCA 퍼지 혼합 모델을 이용한 화자 식별)

  • Lee, Ki-Yong
    • Speech Sciences
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    • v.10 no.4
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    • pp.149-157
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    • 2003
  • In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker's PCA transformation matrix to reduce the correlation among the elements. Then, the fuzzy mixture model for speaker is obtained from these transformed feature vectors with reduced dimensions. The orthogonal Gaussian Mixture Model (GMM) can be derived as a special case of PCA fuzzy mixture model. In our experiments, with having the number of mixtures equal, the proposed method requires less training time and less storage as well as shows better speaker identification rate compared to the conventional GMM. Also, the proposed one shows equal or better identification performance than the orthogonal GMM does.

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A Neuro-Fuzzy System Modeling using Gaussian Mixture Model and Clustering Method (GMM과 클러스터링 기법에 의한 뉴로-퍼지 시스템 모델링)

  • Kim, Sung-Suk;Kwak, Keun-Chang;Ryu, Jeong-Woong;Chun, Myung-Geun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.6
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    • pp.571-576
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    • 2002
  • There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.