Celal Yildiz

NEURAL MODELS FOR COPLANAR STRIP LINE SYNTHESIS

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.

Artificial Neural Networks for Calculating the Characteristic Impedance of Air-Suspended Trapezoidal and Rectangular-Shaped Microshield Lines

Neural models for calculating the characteristic impedance of air-suspended trapezoidal and rectangular-shaped microshield lines, based on the multilayered perceptrons (MLPs), are presented. Six learning algorithms, bayesian regulation (BR), Levenberg-Marquardt (LM), quasi-Newton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Powell (CGF), are used to train the MLPs. The characteristic impedance results obtained by using neural models are in very good agreement with the results available in the literature. When the performances of neural models are compared with each other, the best test result is obtained from the MLPs trained by the BR algorithm.

QUASI-STATIC MODELS BASED ON ARTIFICIAL NEURAL NEWORKS FOR CALCULATING THE CHARACTERISTIC PARAMETERS OF MULTILAYER CYLINDRICAL COPLANAR WAVEGUIDE AND STRIP LINE

In this paper, two different neural models are proposed for calculating the quasi-static parameters of multilayer cylindrical coplanar waveguides and strip lines. These models were basically developed by training the artificial neural networks with the numerical results of quasi-static analysis. Neural models were trained with four different learning algorithms to obtain better performance and faster convergence with simpler structure. When the performances of neural models are compared with each other, the best test results are obtained from the multilayered perceptrons trained by the Levenberg- Marquardt algorithm. The results obtained from the neural models are in very good agreements with the theoretical results available in the literature.

A Multilayered Perceptron Neural Network for a Micro-Coplanar Strip Line

This paper presents a new approach based on multilayered perceptrons (MLPs) to compute the characteristic impedance and the effective permittivity of the micro-coplanar strip. Only one neural model is used to calculate the both parameters of the strip. An extended delta-bar-delta algorithm is used to train the MLP. The results obtained by using the MLP model are in very good agreement with the theoretical and experimental results reported elsewhere.

Neural Models for the Elliptic- and Circular-Shaped Microshield Lines

This article presents a new approach based on artificial neural networks (ANNs) to calculate the characteristic parameters of elliptic and circular-shaped microshield lines. Six learning algorithms, bayesian regularization (BR), Levenberg-Marquardt (LM), quasi- Newton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Reeves (CGF), are used to train the ANNs. The neural results are in very good agreement with the results reported elsewhere. When the performances of neural models are compared with each other, the best and worst results are obtained from the ANNs trained by the BR and CGF algorithms, respectively.

ADAPTIVE NEURO-FUZZY MODELS FOR CONVENTIONAL COPLANAR WAVEGUIDES

In this work a new method based on the adaptive neuro-fuzzy inference system (ANFIS) was successfully introduced to determine the characteristic parameters, effective permittivities and characteristic impedances, of conventional coplanar waveguides. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid-learning algorithm, which combines least-square method and backpropagation algorithm, is used to identify the parameters of ANFIS. There are very good agreement between the results of ANFIS models, experimental works, conformal mapping technique, spectral domain approach and a commercial electromagnetic simulator, MMICTL.

Neural models for an asymmetric coplanar stripline with an infinitely wide strip

This paper presents a new approach, based on artificial neural networks (ANNs), to determine the characteristic impedance and the effective permittivity of an asymmetric coplanar stripline (ACPS) with an infinitely wide strip. ANNs are trained with five learning algorithms to obtain better performance and faster convergence with simpler structure. The best results for training and test were obtained from the models trained with the Levenberg–Marquardt and the Bayesian regularization algorithms. The results obtained by using the neural model are in very good agreement with the results available in the literature. The neural models presented in this work provide simplicity and accuracy to determine both the parameters of an ACPS. The method is not time consuming and is easily included in a CAD system.

SYNTHESIS FORMULAS FOR MULTILAYER HOMOGENEOUS COUPLING STRUCTURE WITH GROUND SHIELDING

This paper presents new and accurate synthesis formulas for the multilayer homogeneous coupling structure with ground shielding (MHCS-WGS). The synthesis formulas are obtained by means of a differential evolution algorithm (DEA), and are useful to microwave engineers for accurately calculating the physical dimensions of MHCS-WGS. The average percentage error is calculated to be 0.8% for 13614 MHCS-WGS samples having different electrical parameters and physical dimensions, as compared with the results of quasi-static analysis.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR THE COMPUTATION OF THE CHARACTERISTIC IMPEDANCE AND THE EFFECTIVE PERMITTIVITY OF THE MICRO-COPLANAR STRIP LINE

A method based on adaptive neuro-fuzzy inference system (ANFIS) for computing the effective permittivity and the characteristic impedance of the micro-coplanar strip (MCS) line is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The effective permittivity and the characteristic impedance results obtained by using ANFIS are in good agreement with the theoretical and experimental results reported elsewhere.

COMPARISON OF ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM MODELS FOR ANALYSIS OF CONDUCTOR-BACKED ASYMMETRIC COPLANAR WAVEGUIDES

A method based on adaptive-network-based fuzzy infer- ence system (ANFIS) is presented for the analysis of conductor- backed asymmetric coplanar waveguides (CPWs). Four optimization algorithms, hybrid learning, simulated annealing, genetic, and least- squares, are used to determine optimally the design parameters of the ANFIS. The results of ANFIS models are compared with the results of conformal mapping technique, a commercial electromagnetic simula- tor IE3D, and the experimental works realized in this study. There is very good agreement among the results of ANFIS models, quasi-static method, IE3D, and experimental works. The proposed ANFIS models are not only valid for conductor-backed asymmetric CPWs but also valid for conductor-backed symmetric CPWs.