Fuzzy self-organizing map

Vuorimaa, Petri

doi:10.1016/0165-0114(94)90312-3

Cited by 106 publications

(22 citation statements)

References 14 publications

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“…b m small ð:Þ denotes the antecedent membership function for small and f small ð:Þ denotes the linear or nonlinear functions for the consequent part of this rule. Lin and Lee (1991) Bell AND/product Bell WA, COA Berenji and Khedkar (1992) Triangular AND/soft-min Triangular LMOM Nie and Linkens (1993) Bell AND/product Singleton WA Jang (1993) Bell AND/product Singleton WA Vuorimaa (1994) Triangular AND/min Singleton WA Horikawa et al (1992) Bell AND/product Singleton WA,TSK FREN G/Sigmoid -LCs PS Note: MF, WA, COA, LMOM, TSK and PS denote the membership function, the weight average, the center of area, the localized mean of maximum, the Tsukamoto defuzzification (Horikawa et al, 1992;Takagi and Sugeno, 1992) and parallel structure, respectively. Bell, Triangular and Singleton are the types of membership functions.…”

Section: Article In Pressmentioning

confidence: 98%

Adaptive controller with fuzzy rules emulated structure and its applications

Treesatayapun

Uatrongjit

2005

Engineering Applications of Artificial Intelligence

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Section: Article In Pressmentioning

confidence: 98%

Adaptive controller with fuzzy rules emulated structure and its applications

Treesatayapun

Uatrongjit

2005

Engineering Applications of Artificial Intelligence

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“…Thus we find proposals that use fuzzy neural networks, 4,5 fuzzy subset theory combined with descent gradient techniques, or with clustering techniques, 7,6,8 etc . .…”

Section: Fuzzy Modeling and Agentsmentioning

confidence: 98%

A multiagent architecture for fuzzy modeling

Delgado

Gmez-Skarmeta

Marn-Blzquez

et al. 1999

Int. J. Intell. Syst.

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In this paper a hybrid learning system that combines different fuzzy modeling techniques is being investigated. In order to implement the different methods, we propose the use of intelligent agents, which collaborate by means of a multiagent architecture. This approach, involving agents which embody the different problem solving methods, is a potentially useful strategy for enhancing the power of fuzzy modeling systems. ᮊ 1999 John Wiley & Sons, Inc.

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“…Although there has been a large amount of work on fuzzy SOM (e.g. [5,6]), the fuzzy SOM models reported in the literature do not address the problem of incomplete data. Also, the feature maps generated by these fuzzy SOM models are virtually crisp.…”

Section: Introductionmentioning

confidence: 97%

Application of self-organising maps for data mining with incomplete data sets

Wang

2003

Neural Computing & Applications

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Self-organising maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.

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Fuzzy self-organizing map

Cited by 106 publications

References 14 publications

Adaptive controller with fuzzy rules emulated structure and its applications

Adaptive controller with fuzzy rules emulated structure and its applications

A multiagent architecture for fuzzy modeling

Application of self-organising maps for data mining with incomplete data sets

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