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In this study, accurate and very simple quasi-static analysis models developed with genetic programming (GP) are presented in order to determine the effective dielectric constant and characteristic impedance of dielectric base supported coplanar waveguides. In order to demonstrate the validity and accuracy of the quasi-static analysis models improved by using GP, the results of proposed models in this work were compared with the quasi-static analysis results available in the literature. These comparison results show that there is a good agreement between the results of the proposed quasi-static analysis models and the quasi-static analysis results available in the literature. The design parameter ranges in this study are 2 ≤ εr1 ≤ 10, 10 ≤ εr2 ≤ 20, 20 µm ≤ h ≤ 2000 µm, 0.1 ≤ w/h ≤ 1, 0.1 ≤ s/h ≤ 4 and the characteristic impedance values of dielectric base supported coplanar waveguides corresponding these design parameter values are between 19 Ω ≤ Z0 ≤ 117 Ω. It has been observed that the accuracy of the proposed quasi-static analysis models for dielectric base supported coplanar waveguides proposed in this study is good enough for the practical applications., Bu çalışmada dielektrik taban destekli eş düzlemli dalga kılavuzlarının (DTDEDDK) efektif dielektrik sabitini ve karakteristik empedansını belirlemek amacıyla genetik programlama (GP) ile geliştirilmiş doğru ve çok basit quasi-statik analiz modelleri sunulmuştur. GP kullanılarak elde edilen quasi-statik analiz modellerinin geçerliliğini ve doğruluğunu göstermek için, bu çalışmada sunulan modelden elde edilen sonuçlar literatürde var olan quasi-statik analiz sonuçları ile karşılaştırılmıştır. Bu karşılaştırma sonuçları önerilen quasi-statik analiz modellerinin sonuçları ile literatürdeki mevcut quasi-statik analiz sonuçları arasında iyi bir uyum olduğunu göstermiştir. Bu çalışmadaki tasarım parametrelerinin değer aralıkları 2 ≤ εr1 ≤ 10, 10 ≤ εr2 ≤ 20, 20 µm ≤ h ≤ 2000 µm, 0.1 ≤ w/h ≤ 1, 0.1 ≤ s/h ≤ 4 ve DTDEDDK’larının bu parametre değerlerine karşılık gelen karakteristik empedans değerleri 19 Ω ≤ Z0 ≤ 117 Ω aralığındadır. Bu çalışmada önerilen DTDEDDK’lar için önerilen quasi-statik analiz modellerinin doğruluklarının pratik uygulamalar için yeterince iyi olduğu gözlemlenmiştir.

Very simple computer-aided-design models are introduced to determine the characteristic parameters such as effective permittivities and characteristic impedances of unshielded suspended and inverted microstrip lines. Computeraided-design models are determined with the use of the genetic programming. The results of computer-aided-design models are compared with the results of quasi-static analysis, experimental works available in the literature and a commercial electromagnetic simulator. The comparison results clearly show that computer-aided-design models proposed in this work are in very good agreement with the simulation, theoretical and experimental results for the suspended and inverted microstrip lines. The design parameter ranges in this work are 2

Accurate and very simple synthesis formulas for the microcoplanar strip (MCS) lines are presented. They are very useful for microwave engineers to easily and accurately compute the physical dimension of MCS lines. The coefficients in the synthesis formulas are determined with the use of artificial bee colony algorithm. The results of the synthesis formulas are compared with the results of respective quasi-static analysis, other synthesis formulas, and experimental works available in the literature to show the validation of synthesis formulas presented in this article. The average percentage error is calculated 3.07% for 864 MCS line samples having different electrical parameters and physical dimensions. (C) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:615619, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27377

This article presents very simple closed-form design equations to calculate the geometrical dimensions of asymmetric coplanar strip line with an infinitely wide strip (CPSIWS). The coefficients of the design equations are determined by artificial bee colony algorithm (ABCA) that is a powerful optimization technique. The results of the proposed design equations are compared with the results of other design equations, experimental works previously published in the literature, quasi-static analysis in the literature, and IE3D software. Accuracy of the characteristic impedances calculated by the design equation results proposed in this work is found to be better than 0.53% with respect to the desired characteristic impedances for 1,960 asymmetric CPSIWS samples.

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. (C) 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett, 54:460-467, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26530

New and accurate synthesis formulas for the asymmetric conductor-backed coplanar waveguide (ACBCPW) are presented. They are obtained by using a differential evolution and particle swarm optimization algorithms. The synthesis formulas are useful to microwave engineers for accurately calculating the physical dimensions of ACBCPWs. The results of the synthesis formulas are compared with the quasi-static analysis, a full-wave electromagnetic simulator IE3D, and experimental results obtained in this work. The average percentage errors of the synthesis formulas found by using differential evolution and particle swarm optimization algorithms are calculated to be 0.56% and 0.93%, respectively, for 3024 ACBCPW samples having different electrical parameters and physical dimensions, compared with the results of respective quasi-static analysis. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:211–216, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25666

In this work, computer-aided design models based on adaptive-network-based fuzzy inference system (ANFIS) for the quasistatic analysis of three different coplanar strip line structures are presented. These strip line structures are conventional coplanar strip lines, asymmetrical coplanar strip lines with infinitely wide strip, and asymmetrical coplanar strip lines with infinitely thick dielectric substrate. The design parameters of the proposed ANFIS models are optimally determined by using four different optimization algorithms, hybrid learning, simulated annealing, least-squares, and genetic algorithm. 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 hybrid learning algorithm. There is a good agreement among the results of ANFIS models, quasistatic analysis, a full-wave. electromagnetic simulator IE3D, and experimental works realized in this study. (C) 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1990-1996,2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25372

In this article, adaptive-network-based frizzy inference system (ANFIS) models are proposed to compute the characteristic impedances of air-suspended trapezoidal-shaped microshield lines (AS-TSMLs) and air-suspended rectangular-shaped microshield lines (AS-RSMLs). The ANFIS is a fuzzy inference system implemented within the architecture and optimization procedure of adaptive networks. Four optimization algorithms, hybrid learning, simulated annealing, genetic, and least-squares, are used to determine optimally the design parameters of ANFIS. The characteristic impedance results of ANFIS models are in very good agreement with the results available in the literature. When the performances of ANFIS models are compared with each other, the best results are obtained from the ANFIS model trained by the hybrid learning algorithm. (C) 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 20-24, 2010; Published online in Wiley Inter-Science (www.interscience.wiley.com). DOI 10.1002/mop.24829

In this paper, accurate synthesis formulas obtained by using a difierential evolution (DE) algorithm for conductor-backed coplanar waveguides (CBCPWs) are presented. The synthesis formulas are useful to microwave engineers for accurately calculating the physical dimensions of CBCPWs. The results of the synthesis formulas are compared with the theoretical and experimental results available in the literature. A full-wave electromagnetic simulator IE3D and experimental results are obtained in this work. The average percentage error of the synthesis formulas obtained by using DE algorithm is computed as 0.67% for 1086 CBCPW samples having difierent electrical parameters and physical dimensions, as compared with the results of quasi-static analysis.

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.

New and accurate synthesis formulas for the asymmetric coplanar stripline with an infinitely wide strip (ACPS-IWS) are presented. They are obtained by using a differential evolution algorithm (DEA) and particle swarm optimization (PSO) algorithm, and are useful to microwave engineers for accurately computing the physical dimensions of ACPS-IWS. The average percentage errors of the synthesis formulas found by using DEA and PSO are calculated to be 0.85% and 1.62%, respectively, for 4913 ACPS-IWS samples having different electrical parameters and physical dimensions, as compared with the results of quasi-static analysis.

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.

Simple and accurate closed-form formulas obtained by using a differential evolution algorithm are presented for the synthesis of coplanar waveguides (CPW). The results of the synthesis formulas proposed in this article are compared with those of the quasi-static analysis, the synthesis formulas reported by the other researchers and also the experimental works available in the literature. The accuracy of the proposed synthesis formulas is found to be better than 0.75% for 9256 CPWs samples. (C) 2008 Wiley Periodicals, Inc.

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.

Neural models based on multilayered perceptrons (MLPs) for computing the effective permittivities and the characteristic impedances of both the conventional coplanar waveguides (CCPWs) and the supported coplanar waveguides (SCPWs) are presented. Six learning algorithms, Levenberg-Marquardt (LM), Bayesian regularization (BR), quasi-Newton (QN), scaled conjugate gradient (SCG), conjugate gradient of Fletcher-Powell (CGF), and resilient propagation (RP), are used to train the MLPs. The results of neural models presented in this paper are compared with the results of the experimental works, the conformal mapping technique (CMT), the spectral domain approach (SDA), and three commercial electromagnetic simulators such as IE3D, CAPIND2D, and MMICTL. The neural results are in very good agreement with the theoretical and experimental results. When the performances of neural models are compared with each other, the best result is obtained from the MLPs trained by the LM algorithm. (c) 2006 Elsevier GmbH. All rights reserved.

New, accurate closed-form formulas for the synthesis of multilayer homogeneous coupling structure (MHCS) are presented. They are obtained by using a differential evolution algorithm (DEA), and are useful for the computer-aided design (CAD) of MHCS. The average percentage error is found to be 0.38% for 1825 MHCS samples having different electrical parameters and physical dimensions, as compared with the results of the quasi-static analysis. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2486–2489, 2007; Published online in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/mop.22743

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.

In this work, a simple, fast, and accurate optimization approach based on a differential evolution (DE) algorithm is presented to obtain closed-form formulas for the synthesis of coplanar strip (CPS) lines. The results obtained from the synthesis formulas proposed in this work are compared with the results of quasi-static analysis and the other synthesis formulas previously published in the literature. The accuracy of the proposed synthesis formulas is found to be better than 1.22% for 2524 CPS samples. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1133–1137, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21559

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.

This paper presents new, very simple, accurate closed-form formulas obtained by using the curve-fitting technique for coplanar waveguide synthesis. The results achieved from this work are compared to the results obtained from the quasi-static analysis and the synthesis formulas proposed by other researchers, as well as experimental works available in the literature. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 49–53, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20043

Very simple and accurate closed-form formulas obtained by using the curve-fitting technique are presented for coplanar strip (CPS) synthesis. The average percentage error is found to be 1.56% for 2524 CPS samples having different electrical parameters and physical dimensions, as compared with the results obtained from the quasi-static analysis. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 44: 199–202, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20586

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.

Artificial neural networks (ANNs) have been promising tools for many applications. In recent years, a computer-aided design approach based on (ANNs) has been introduced to microwave modelling, simulation and optimization. In this work, the characteristic parameters of top shielded multilayered coplanar waveguides (CPWs) have been determined with the use of ANN models. These neural models were trained with Levenberg-Marquardt, resilient propagation, Bayesian regulation, quasi-Newton, and backpropagation learning algorithms. Better performance and learning speed with a simpler structure were achieved from these models. The results have shown that the estimated characteristic parameters are in very good agreement with the computed results by using conformal mapping theory. The Levenberg-Marquardt learning algorithm was found to be the best algorithm among all. As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristic parameters of the top shielded multilayered CPWs.

Simple and accurate models based on adaptive-network-based fuzzy inference system (ANFIS) to compute the physical dimensions of open supported coplanar waveguides are presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems. Four optimization algorithms, hybrid learning, simulated annealing, least-squares, and genetic, are used to determine optimally the design parameters of the ANFIS. When the per- formances of ANFIS models are compared with each other, the best results are obtained from the ANFIS models trained by the hybrid learning algorithm. The results of ANFIS are compared with the results of the conformal mapping tech- nique, the rigorous spectral-domain hybrid mode analysis, the improved spectral domain approach, the synthesis formulas, a full-wave electromagnetic simulator IE3D, and experimental works realized in this study.

In this article, new, and accurate synthesis formulas to compute the physical dimensions of open supported coplanar waveguides (OS-CPWs) are presented. The synthesis formulas are obtained with the use of differential evolution (DE) and particle swarm optimization (PSO) algorithms. They are useful for the computer-aided design of OS-CPWs. The average percentage errors of the synthesis formulas obtained by using DE and PSO algorithms are computed to be 1.26% and 1.67%, respectively, for 4560 OS-CPW samples having different electrical parameters and physical dimensions, as compared with the results of quasi-static analysis. (C) 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 262-269, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24908

A method based on adaptive-network-based fuzzy inference system (ANFIS) is presented for the quasi-static analysis of conductor-backed coplanar waveguides. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems. Pour optimization algorithms, hybrid learning, genetic, simulated annealing and least-squares, are used to determine optimally the design parameters of the ANFIS. The results of ANFIS are compared with the results of experimental works, quasi-static and fulll-wave spectral domain approaches, conformal mapping technique, and a commercial electromagnetic simulator IE3D. The results of ANFIS models are in very good agreement with the results of other methods and experimental works. When the performances of ANFIS models are compared with each other, the hest result is obtained from the ANFIS model trained by the hyhrid learning algorithm. (C) 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 439-445, 2009 Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24059

This article presents new and accurate synthesis formulas for the microcoplanar stripline (MCS). The synthesis formulas are obtained by means of a differential evolution algorithm, and are useful to microwave engineers for accurately calculating the physical dimensions of MCS. The average percentage error is calculated to be 0.9% for 2254 MCS samples having different electrical parameters and physical dimensions, as compared with the results of the quasi-static analysis. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2884–2888, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23823

Simple and accurate formulas are presented for the synthesis of conductor-backed coplanar waveguide (CPW). These formulas are derived by applying function approximation and curve-fitting technique to the respective quasi-static analysis results. The results obtained from these formulas are compared with the results of quasi-static analysis and experimental works available in the literature. Average percentage error is found to be better than 1% for 1086 conductor-backed CPW samples having different electrical parameters and geometrical dimensions. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1115–1117, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23304

Neural models based on the artificial neural networks (ANNs) for computing the effective permittivities and characteristic im- pedances of V-shaped conductor-backed coplanar waveguides are pre- sented. The proposed neural models can also be used for calculating the characteristic parameters of conductor-backed coplanar waveguides. Six learning algorithms, Levenberg-Marquardt, bayesian regularization, quasi-Newton, scaled conjugate gradient, conjugate gradient of Fletcher-Reeves, and resilient propagation, are used to train the ANNs. The neural results 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 result is obtained from the ANNs trained by the LM algorithm. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1294 -1299, 2007; Published online in Wiley Inter- Science (www.interscience.wiley.com). DOI 10.1002/mop.22473

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), quasi-Newton (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.

This article presents a new approach based on multilayered perceptron neural networks (MLPNNs) to calculate the odd- and even-mode characteristic impedances and effective permittivities of the broadside-coupled V-shaped microshield coplanar waveguides (BC-VSMCPWs). 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 MLPN-Ns. 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 MLPNNs trained by the BR and CGF algorithms, respectively.

Summary In this paper a computer-aided design (CAD) approach based on artificial neural networks (ANNs) was successfully introduced to determine the characteristic parameters of shielded multilayered coplanar waveguides (SMCPWs). ANNs are trained with four 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 Bayesian regularization and Levenberg-Marquardt algorithms. The neural model results are in very good agreement with the results available in the literature for SMCPWs and three other different shielded CPW structures. One can calculate the quasi-static parameters of these four different shielded CPW configurations using only one neural model proposed in this work, easily, simply and accurately.

This paper presents new and simple models based on artificial neural networks (ANNs) to determine the effective permittivities of suspended microstrip (SM) and inverted microstrip (IM) lines. The neural results are in very good agreement with the theoretical and experimental results available in the literature for both SM and IM lines. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 39: 383–389, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.11225

This article presents a new approach based on artificial neural networks (ANNs) to determine the characteristic parameters of symmetric and asymmetric coplanar waveguides with a finite dielectric thickness. ANNs are trained with the use of five training algorithms. The results obtained from neural models are in very good agreement with the theoritical and experimental results available in the literature. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 438–446, 2003.

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.

Summary Neural models for computing the resonant frequency of electrically thin and thick circular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Five learning algorithms, delta-bar-delta, extended delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The resonant frequency results of neural models are in very good agreement with the experimental results available in the literature. In this paper, the characteristic impedance and the effective permittivity of the asymmetric coplanar waveguide backed with a conductor are also computed by using only one neural model trained by the backpropagation with momentum and the extended delta-bar-delta algorithms. When the performances of neural models are compared with each other, the best results for test are obtained from the multilayered perceptrons trained by the extended delta-bar-delta algorithm.

A very simple model for the input impedance of a coax-fed rectangular microstrip patch antenna is presented. It is based on the cavity model and the equivalent resonant circuits. The theoretical input impedance results obtained from this model are in good agreement with the experimental results available in the literature. This model is well suited for computer-aided design (CAD).

The second harmonic power generation efficiency (SHPGE) obtained under the phase-matching conditions in the 4-N,N-dimethylamino-3-acetamidonitroberzene (DAN) crystal-cored fibers having different cladding materials has been investigated. It has been found that a very high SHPGE can be obtained under the certain conditions. For the crystal-cored fiber having the core diameter of 0.52 µ m, the length of 2 cm, and the input beam power 3 mW, the efficiency of about 100% has been estimated for all of the cladding materials employed in this paper.

A new approach based on artificial neural networks is successfully introduced to determine the characteristic parameters of a coplanar waveguide (CPW) sandwiched between two dielectric substrates. Neural models were trained with eight different learning algorithms to obtain better performance and faster convergence with a simpler structure. The best results were obtained from the models trained with Levenberg-Marquardt and Bayesian regulation learning algorithms. The results obtained from the neural model are in very good agreement with theoretical and experimental results available in the literature. The presented neural model is valid for both conventional and sandwiched CPWs.

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.