Anfis models for the quasistatic analysis of coplanar strip line structures

Türkmen, Mustafa; Yildiz, Celal; Güney, Kerïm; Kaya, Sabri

doi:10.1002/mop.25372

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The ANFIS can simulate and analyse the mapping relation between the input and output data through a learning to determine optimal parameters of a given FIS. Fast and accurate learning, excellent explanation facilities in the form of semantically meaningful fuzzy rules, the ability to accommodate both data and existing expert knowledge about the problem, and good generalization capability features have made neuro‐fuzzy systems popular in recent years [28–42].…”

Section: Adaptive‐network‐based Fuzzy Inference Systemmentioning

confidence: 99%

ANFIS models for synthesis of micro‐coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip

Kaya

Güney

Yildiz

et al. 2011

Micro & Optical Tech Letters

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In this letter, computer-aided design models based on adaptive-network-based fuzzy inference system (ANFIS) for the synthesis of two coplanar stripline structures are presented. These coplanar stripline structures are micro-coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip. Four optimization algorithms, hybrid learning (HL), simulated annealing, least-squares, and genetic, are used to determine optimally the design parameters of the ANFIS models. The results of ANFIS models are compared with the results of quasi-static analysis, synthesis formulas available in the literature, a full-wave electromagnetic simulator IE3D, and experimental works realized in this study. The results of ANFIS models agree with the results of other methods and experimental works. When the performances of ANFIS models are compared with each other, the best results for training and test are obtained from the models trained with HL algorithm.

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Section: Adaptive‐network‐based Fuzzy Inference Systemmentioning

confidence: 99%

ANFIS models for synthesis of micro‐coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip

Kaya

Güney

Yildiz

et al. 2011

Micro & Optical Tech Letters

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show abstract

“…The ANFIS can simulate and analysis the mapping relation between the input and output data through a learning to determine optimal parameters of a given FIS. Fast and accurate learning, excellent explanation facilities in the form of semantically meaningful fuzzy rules, the ability to accommodate both data and existing expert knowledge about the problem, and good generalization capability features have made neuro-fuzzy systems popular in recent years [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. In this paper, four different optimization algorithms, hybrid learning (HL) algorithm [28,29], simulated annealing (SA) [44] algorithm, least-squares (LSQ) algorithm [45,46], and genetic algorithm (GA) [47,48], are used to train the ANFIS.…”

Section: Introductionmentioning

confidence: 99%

Anfis Models for Synthesis of Open Supported Coplanar Waveguides

Kaya

Güney

Yildiz

et al. 2013

NNW

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Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

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