Artificial Neural Network Models for Coplanar Stripline Synthesis

Salivahanan, S.; Ramesh, Sai G.; Karthikeyan, S.; Raju, S.; Abhaikumar, V.

doi:10.1080/09747338.2002.11415757

Cited by 2 publications

(5 citation statements)

References 8 publications

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“…The class of ANN and/or architecture selected for a particular model implementation depends on the problem to be solved. After several experiments using different architectures coupled with different training algorithms, in this paper, the multilayered perceptron (MLP) neural network architecture is used in calculating the electrical parameters and physical dimensions of CPSs.Neural model for the CPS synthesis was introduced for the first time by Salivahanan et al [10]. This neural model has some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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ANN models for coplanar strip line analysis and synthesis

Narayana

Krishna

Reddy

2008

2008 International Conference on Recent Advances in Microwave Theory and Applications

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Neural networks recently gained attention as fast and flexible vehicles to microwave modeling, simulation and optimization. After learning and abstracting from microwave data, through a process called training, neural network models are used during microwave design to provide instant answers to the task learned. This paper presents simple and accurate ANN models for the analysis and synthesis of CPS structures to very accurately compute the characteristic parameters and the physical dimensions respectively for the required design specifications.

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Section: Introductionmentioning

confidence: 99%

“…It is not possible to design CPSs having small characteristic impedances (ZO < 70 Q) .Thus, this neural model is not suitable for practical ranges. Moreover, the neural model proposed in [10] was trained using only one learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

ANN models for coplanar strip line analysis and synthesis

Narayana

Krishna

Reddy

2008

2008 International Conference on Recent Advances in Microwave Theory and Applications

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“…Neural model for the CPS synthesis was introduced for the first time by Salivahanan et al [24]. This neural model has some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…ANN is a very powerful approach for building complex and nonlinear relationship between a set of input and output data [13]. Analysis [14][15][16][17][18][19][20] and synthesis models [21][22][23][24] based on ANNs have been presented for various coplanar transmission lines. In these applications, ANNs have more general functional forms and are usually better than the classical techniques; also, they provide simplicity in real-time operation.…”

Section: Introductionmentioning

confidence: 99%

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Neural Models for Coplanar Strip Line Synthesis

Yildiz¹,

Güney²,

Türkmen³

et al. 2007

PIER

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Abstract-Simple and accurate models based on artificial neural networks (ANNs) are presented to accurately determine the physical dimensions of coplanar strip lines (CPSs). Five learning algorithms, Levenberg-Marquardt (LM), bayesian regularization (BR), quasiNewton (QN), conjugate gradient with Fletcher (CGF), and scaled conjugate gradient (SCG), are used to train the neural models. The neural results are compared with the results of the quasi-static analysis and the synthesis formulas available in the literature. The accuracy of the neural model trained by LM algorithm is found to be better than 0.24% for 10614 CPS samples.

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Artificial Neural Network Models for Coplanar Stripline Synthesis

Cited by 2 publications

References 8 publications

ANN models for coplanar strip line analysis and synthesis

ANN models for coplanar strip line analysis and synthesis

Neural Models for Coplanar Strip Line Synthesis

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