A method for automatic rule and membership function generation by discretionary fuzzy performance function and its application to a practical system

Iokibe, Tadashi

doi:10.1109/ijcf.1994.375087

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Partitional clustering methods, which divide the data according to natural classes present in it, have been used in a large variety of scientific disciplines and engineering applications, among them pattern recognition (Duda & Hart, 1973), learning theory (Moody & Darken, 1989), astrophysics (Dekel & West, 1985), medical imaging (Suzuki, Shibata, & Suto, 1995) and data processing (Phillips et al, 1995), machine translation of text (Cranias, Papageorgiou, & Piperdis, 1994), image compression (Karayiannis, 1994), satellite data analysis (Baraldi & Parmiggiani, 1995), automatic target recognition (Iokibe, 1994), and speech recognition (Kosaka & Sagayama, 1994) and analysis (Foote & Silverman, 1994).…”

Section: Introductionmentioning

confidence: 99%

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Data Clustering Using a Model Granular Magnet

1997

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We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures, it is completely ordered; all spins are aligned. At very high temperatures, the system does not exhibit any ordering, and in an intermediate regime, clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.

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Section: Introductionmentioning

confidence: 99%

“…al. 1994), satellite data analysis (Baraldi and Parmiggiani 1995), automatic target recognition (Iokibe 1994), as well as speech recognition (Kosaka and Sagayama 1994), and analysis (Foote and Silverman 1994).…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Using a Model Granular Magnet

1997

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“…From the mathematical point of view, the membership functions , and seem to be assembled by piecewise linear functions. These partitioned membership functions denoted as , and can be expressed as (24) where denotes the trace of a matrix, and is the partition matrix defined as (25) shown at the bottom of the page. in which denotes the step size of the input domain partition, and is the unit step function of defined as (26) The membership function in each interval is now modified by its corresponding hedge operator which is the -th element of the hedge combination vector defining the proper hedge operators of the intervals of the whole input domain.…”

Section: Linguistic Hedge Fuzzy Logic Controller Architecturementioning

confidence: 99%

“…Chang [23] adopted the GA-based tuning methods to membership function tuning. Iokibe [24] automatically generated the membership function by means of the fuzzy clustering method which produces a much richer construction efficiency than the neural network approach [25]. Krishnapuram [26] relied on the properties of possibilistic clustering to develop an approach for generating membership functions.…”

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confidence: 99%

Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms

Liu

Chen

Tsao

2001

IEEE Trans. Syst., Man, Cybern. B

112

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In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.

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Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

Khoury

Liu

2010

Advanced Information and Knowledge Processing

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A method for automatic rule and membership function generation by discretionary fuzzy performance function and its application to a practical system

Cited by 10 publications

References 3 publications

Data Clustering Using a Model Granular Magnet

Data Clustering Using a Model Granular Magnet

Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms

Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

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