S. Miyamoto

University of Tsukuba, Tsukuba, Ibaraki, Japan

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Publications (4)0 Total impact

  • K. Umayahara · S. Miyamoto · Y. Nakamori
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    ABSTRACT: This paper considers the problem of detecting local linear substructures of a system in a high-dimensional data space by applying a fuzzy clustering technique. We propose a linear fuzzy clustering method using eigenvalues of the fuzzy scatter matrix in the objective function for optimizing the dimensional coefficients. The optimal solutions for the objective function and some illustrative examples are shown in this paper
    No preview · Conference Paper · Aug 2001
  • S. Yoshizawa · T. Kawabe · S. Miyamoto
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    ABSTRACT: We propose a new design method for robust control configured systems with multiple design specifications. Since this design problem is formulated as the multiobjective minimax optimization problem, we use the genetic algorithm (GA) based technique to obtain the optimal solution. This design method is based on the input deviation, the minimax design approach and Pareto-partitioning GA. We apply this design method for designing a 4 wheeled steering car system. Some design examples are included to show the applicability and effectiveness of the proposed method
    No preview · Conference Paper · Aug 2001
  • O. Takata · S. Miyamoto · K. Umayahara
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    ABSTRACT: Fuzzy c-means is well-known among the various methods of fuzzy cluster analysis. L<sub>1</sub>-based fuzzy c-means has also been studied in recent years. This paper discusses the L<sub>1</sub>-based fuzzy c-means of data with fuzzy uncertainties. The data unit is supposed to be the Cartesian product of fuzzy numbers. The metric between a data unit with uncertainty and a cluster center is defined using minimum and maximum distances. The fuzzy c-means algorithm is an alternative procedure for the optimization of the cluster center and the fuzzy set membership, while the solution of the cluster center for uncertain data cannot be obtained directly. An algorithm for the solution of cluster centers based on the L<sub>1</sub> metric for uncertain data is developed in this paper. Using this algorithm, an exact alternate optimization procedure is obtained. Numerical examples show that the results for uncertain data are different from the results for data without uncertainties
    No preview · Conference Paper · Aug 2001
  • S. Miyamoto · K. Umayahara
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    ABSTRACT: A quadratic regularization method is proposed as a variation of the fuzzy c-means. The standard fuzzy c-means is regarded as a regularization of the crisp k-means, and hence other regularization methods can be studied as fuzzy versions of the crisp c-means. A new algorithm for calculating membership values is derived, whereas calculation of cluster centers is similar to the standard method. The nearest prototype classification functions which has been derived from the standard fuzzy c-means is transformed into the corresponding method within the present method of quadratic regularization. It should be noted that the present method yields piecewise linear classification functions
    No preview · Conference Paper · Jun 1998