Medical Sample Classifier Design Using Fuzzy Cerebellar Model Neural Networks

Li, Hsin Yi; Yeh, Rong Guan; Lin, Yu Che; Lin, Lo Yi; Zhao, Jing; Lin, Chih Min; Rudas, Imre J.

doi:10.12700/aph.13.6.2016.6.1

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Adaptive Control Over Non-linear Objects Using the Robust Ne1

Classifying Quantum Structures With Fuzzy Inference System1

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Cited by 5 publications

(2 citation statements)

References 17 publications

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“…This drawback can be eliminated by integrating a CMAC network with an apparatus of fuzzy logic [4]. A similar approach was employed in papers [5][6][7][8]. However, these papers applied traditional training of a network, which, on the one hand, is sufficiently proven when solving rather simple problems, and on the other hand, is very inefficient if there are interferences ξ with a distribution different from Gaussian.…”

Section: Adaptive Control Over Non-linear Objects Using the Robust Nementioning

confidence: 99%

Adaptive control over nonlinear objects using the robust neural network FCMAC

Rudenko

Bezsonov

Lebediev

2018

EEJET

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Section: Adaptive Control Over Non-linear Objects Using the Robust Nementioning

confidence: 99%

Adaptive control over nonlinear objects using the robust neural network FCMAC

Rudenko

Bezsonov

Lebediev

2018

EEJET

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“…This model consists of fuzzy sets and "if-then" constructions. These systems are frequently used and they have various applications, for example [14] [15] [16] [17]. There are many types of fuzzy inference system, for example Mamdani, Sugeno, Tsukamoto etc.…”

Section: Classifying Quantum Structures With Fuzzy Inference Systemmentioning

confidence: 99%

Application of Self-Organizing Maps for Technological Support of Droplet Epitaxy

Ürmös¹,

Farkas²,

Farkas³

et al. 2017

ACTA POLYTECH HUNG

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The subject of this paper is the self-organized grouping of droplet epitaxial III-Vbased nano-structures. For the nano-structure grouping, our developed algorithm -called Quantum Structure Analyzer 1.0 -is used. The operation of this software is based on the principles of the Kohonen Self-Organizing Network. Here, three possibilities for nanostructured groupings are shown. On one hand, we examine the classification of nanostructures with Kohonen Self-Organizing Maps, on the other hand, fuzzy inference systems are applied for the same goal. In the case of the fuzzy methods two approaches are examined in detail. According to the first fuzzy inference approach, the shape factor is calculated from the size of nanostructures. According to the second fuzzy inference approach, the shape factor calculation is based on the controllable parameters of the growth process (eg. pressure and the temperature of the substrate).

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