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Antenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO-SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition-based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs.

In this study, a modified version of salp swarm algorithm (MSSA) is used to synthesize elliptical antenna arrays (EAAs). The original salp swarm algorithm (SSA) is an optimization algorithm inspired by the behavior of salps in nature, which is used to solve engineering problems. The main purpose of the synthesis in this study is to obtain an EAA pattern with low maximum sidelobe levels (MSLs) for a fixed narrow first null beamwidth (FNBW). For different examples, the amplitude and angular position values of the antenna array elements are considered as optimization parameters. To show the effectiveness of the MSSA, eight examples of EAAs with 8, 12, and 20 elements are given. The results obtained with MSSA are compared with those of the antlion optimization, symbiotic organizations search, flower pollination algorithm, and accelerated particle swarm optimization from the literature. It is clear from the numerical results that MSSA outperforms the other algorithms in terms of the suppression of MSL.

This paper presents a study of linear antenna array (LAA) synthesis with a seagull optimization algorithm (SOA) to achieve radiation patterns having low maximum sidelobe levels (MSLs) with and without nulls. The SOA is a new optimization technique based on the moving and attacking behaviors of the seagull in the nature. In this study, the original mathematical model of SOA is modified by compensating the exploration and exploitation features to improve the optimization performance. The optimization ability of the modified SOA (MSOA) is tested with seven numerical examples of LAA. In the first three examples, the amplitude values of the array elements are optimized by MSOA whereas the element position values are calculated by MSOA for the last four examples. The numerical results obtained by MSOA are compared with those of different algorithms from the literature. The results reveal that MSOA algorithm is very good at optimizing antenna array parameters to obtain a desired radiation pattern. Additionally, it is seen that MSOA finds better results than the compared algorithms in terms of MSL and single and multiple null depth levels (NDLs). The contribution of the modification of MSOA is shown with a convergence curve to compare with the original one.

Antenna array synthesis is one of the most popular topics in the electromagnetic field. Since achieving a desired antenna radiation pattern is a mathematical problem, in the literature, there are various optimization algorithms applied to the synthesis process of different kinds of antenna arrays. In this study, Multiverse Optimizer (MVO) and modified MVO (MMVO) are used to perform circular antenna array (CAA) synthesis. During the exploration, exploitation, and local search phases of calculation, MVO uses three concepts in cosmology; white hole, black hole, and wormhole. Convergence capability of this nature-inspired algorithm is employed for finding optimum amplitude and position values of CAA elements in order to achieve an array pattern with low maximum sidelobe level (MSL) and minimum circumference. The performance of MVO and MMVO was tested on five design examples of pattern synthesis, and the obtained results were compared with ten different algorithms. The simulation results show that MVO and MMVO provide low MSLs with small circumferences.

Derin uyku doneminde gorulen uyku igcikleri, kaliteli bir uykunun belirtecidir. Uyku igcikleri, derin uykunun NREM2 evresinde siklikla gorulen kisa sureli olaylardan birisidir. Bu igcikleri, EEG isaretlerinde gorulen yuksek frekansli ve dusuk genlikli bilesenlerdir. Yapilan calismada, yapay sinir aglari kullanilarak uyku sirasinda EEG isaretinin NREM2 evresinde uyku igcikleri belirlenmeye calisilmistir. EEG isaretlerindeki uyku igcikleri, bir uzman tarafindan gorsel olarak isaretlenmistir. Bu calismada, EEG isaretleri yapay sinir agi ile islenmis, ag cikisinin iyilestirilmesi icin morfolojik filtre kullanilmis, elde edilen cikis isareti skorlama islemine tabi tutulmustur. Calismada Sekiz farkli insandan kaydedilen EEG isaretleri kullanilmistir. Bu isaretlerdeki 354 tane isaretlenen uyku igciginden 248 tanesi dogru olarak tespit edilmistir. Yapilan calismanin, Uzman sistemler kullanilarak yapilan uyku skorlamalarinda NREM2 evresinin skorlama basarisini artiracagi dusunulmektedir.

A novel and accurate expression is obtained by employing the differential evolution algorithm for the effective side length (ESL) of the equilateral triangular microstrip antenna (ETMA). This useful formula allows the antenna engineers to accurately calculate the ESL of the ETMA. The computed resonant frequencies (RFs) show very good agreement with the experimental RFs when this accurate ESL formula is utilised for the computation of the RFs for the first five modes.

In this article, a quantized water cycle algorithm (QWCA) is used for the antenna array pattern synthesis with low side-lobe levels (SLLs) and nulls at desired directions by using four-bit digital phase shifters. In addition to the standard features as a metaheuristic algorithm, QWCA has an internal quantization mechanism and a precalculated array factor method. The latter provides an accelerated procedure to QWCA under favour of the digitized values that can be stored in a three-dimensional array. This acceleration is based on the reality that the accessing data in the memory need less time than the usage of the mathematical functions throughout the optimization process. The internal quantization mechanism of QWCA is utilized to achieve digital values matching to the discrete values of the phase shifter instead of the simple rounding up/down routines after optimization. The numerical results showed that QWCA can obtain very good SLLs and null depth levels (NDLs) on the synthesized pattern. Moreover, the results are achieved in remarkably short optimization times. SLL and NDL values obtained by QWCA are also compared with the available literature values. The comparisons reveal that QWCA is able to produce better results than the other compared alternatives. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:21–29, 2015.

A backtracking search optimization algorithm (BSA) is proposed for the synthesis of concentric circular antenna arrays (CCAAs) with the low sidelobe levels at a fixed beamwidth. Several numerical examples of CCAA patterns with the single, multiple, and broad nulls imposed at the directions of interference are also given to illustrate the performance and flexibility of the proposed algorithm. BSA is a relatively new population based evolutionary optimization algorithm. The numerical results show that the design of CCAA using BSA provides good sidelobe levels with a fixed beamwidth. The nulling patterns obtained by BSA are also very good. The quality of results obtained by the BSA is checked by comparing with that of several metaheuristic algorithms in the literature. BSA is also compared with differential search (DS) and bacterial foraging algorithms (BFA) in terms of iterative performances.

Obstructive sleep apnea is a sleep disorder which may lead to various results. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. The mean, frequency and power of signals obtained from patients are frequently used. Obstructive Sleep Apnea of 74 patients were scored in this study. A visual-scoring based algorithm and a morphological filter via Artificial Neural Networks were used in order to diagnose Obstructive Sleep Apnea. After total accuracy of scoring was calculated via both methods, it was compared with visual scoring performed by the doctor. The algorithm used in the diagnosis of obstructive sleep apnea reached an average accuracy of 88.33 %, while Artificial Neural Networks and morphological filter method reached a success of 87.28 %. Scoring success was analyzed after it was grouped based on apnea/hypopnea. It is considered that both methods enable doctors to reduce time and costs in the diagnosis of Obstructive Sleep Apnea as well as ease of use.

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

In this article, a stochastic search technique based on seeker optimization algorithm (SOA) is proposed for null steering of linear antenna arrays by controlling the position-only, phase-only, and amplitude-only. The SOA is relatively new optimization algorithm based on the concept of simulating the act of humans' intelligent search with their memory, experience, and uncertainty reasoning. Several numerical examples of Chebyshev pattern with the single, multiple, and broad nulls imposed at the directions of interference are given to illustrate the performance and flexibility of the proposed algorithm. For a comparison, the nulling patterns obtained by simulated annealing (SA) and tabu search (TS) algorithms are also given. Furthermore, the results of SOA are statistically compared with those of SA and TS algorithms. The statistical results of simulations show that SOA is superior to the other compared algorithms. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011. © 2011 Wiley Periodicals, Inc.

In this letter, new and accurate synthesis formulas to compute the physical dimensions of asymmetric coplanar waveguide (ACPW) are presented. The synthesis formulas are obtained with the use of differential evolution algorithm (DEA), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithm. The synthesis formulas are useful to microwave engineers for accurately calculating the physical dimensions of ACPWs. 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 DEA, GA, PSO, and SA algorithm are calculated to be 0.56, 0.62, 0.87, and 0.88%, respectively, for 2079 ACPW samples having different electrical parameters and physical dimensions, as compared with the results of respective quasi-static analysis. (C) 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1991-1996, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26224

A method based on the bacterial foraging algorithm (BFA) for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling only the element positions. The BFA is a new evolutionary computing technique, based on the foraging behavior of Escherichia (E) coli bacteria in a human intestine. Simulation results for Chebyshev patterns with the imposed single, multiple and broad nulls are given to show the effectiveness of the proposed method. For practical consideration, due to small variations of the element positions, the sensitivity of the produced patterns is also examined by rounding the element position values to the second decimal position.

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 paper, bees algorithm (BA) has been used for null steering in the antenna radiation pattern by controlling the phase-only and the complex weights (both the amplitude and phase) of the array elements. The BA is an optimization algorithm inspired by the behavior of the honey bees to find the optimal way of harvesting food resources around the hive. Simulation results for Chebyshev patterns with the imposed single, multiple and broad nulls are given to show the effectiveness of the proposed method. The sensitivity of the nulling patterns due to small variations of the element phases is also investigated. (C) 2009 Elsevier Ltd. All rights reserved.

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

This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared to the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm.

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.

A method based on concurrent neuro-fuzzy system (CNFS) is presented to calculate simultaneously the resonant frequencies of the rectangular, circular, and triangular microstrip antennas (MSAs). The CNFS comprises an artificial neural network (ANN) and an adaptive-network-based fuzzy inference system (ANFIS). In a CNFS, neural network assists the fuzzy system continuously (or vice versa) to compute the resonant frequency. The resonant frequency results of CNFS for the rectangular, circular, and triangular MSAs are in very good agreement with the experimental results available in the literature.

A method based on adaptive-network-based fuzzy inference system (ANFIS) for computing the input resistance of circular microstrip antennas (MSAs) is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, genetic, differential evolution, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared with the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1253–1261, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23354

In this paper, a method based on clonal selection algorithm (CLONALG) is proposed for null steering of linear antenna arrays by controlling only the positions of selected elements. The CLONALG is a relatively novel population-based evolutionary algorithm inspired by the clonal selection principle of the human immune system. In order to illustrate the accuracy and flexibility of the proposed algorithm, several numerical examples of Chebyshev pattern with the single and double nulls imposed at the directions of interference are given.

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

Multilayered-perceptron-based neural models for calculating the input resistance of electrically thin and thick rectangular microstrip antennas are presented. Eleven learning algorithms—Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, Bayesian regularization, Broyden-Fletcher-Goldfarb-Shanno, conjugate gradient of Polak-Ribiere, conjugate gradient of Powell-Beale, scaled conjugate gradient, one-step secant, resilient backpropagation, backpropagation with momentum, and backpropagation with adaptive learning rate—are used to train the multilayered perceptrons. The input resistance results obtained with the use of neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by the Levenberg-Marquardt algorithm.

In this paper, a clonal selection algorithm (CLONALG) based on clonal selection principle of immune system has been used for null steering in the antenna radiation pattern by controlling only the element positions of a linear array. Numerical examples of Chebyshev pattern with the single, multiple and broad nulls imposed at the directions of interference are given to show the accuracy and flexibility of the CLONALG. The sensitivity of the achieved patterns due to small variations of the element positions is also investigated by truncating the element positions.

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.

An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

A new formula for the wide aperture dimension of a pyramidal horn is presented. It is obtained by using a particle-swarm optimization (PSO) algorithm, and is used in successfully designing optimum-gain pyramidal horns. The computed gains of the designed pyramidal horns are in very good agreement with the desired gains. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1201–1205, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21580

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.

In this paper, a tabu search tracker with adaptive neurofuzzy inference system (TST-ANFIS) is presented for multiple target tracking (MTT). First, the data association problem, formulated as an N-dimensional assignment problem, is solved using the tabu search algorithm (TSA), and then the inaccuracies in the estimation are corrected by the adaptive neuro-fuzzy inference system (ANFIS). The performances of the TST-ANFIS, the joint probabilistic data association filter (JPDAF), the tabu search tracker (TST), Lagrangian relaxation algorithm (LRA), and cheap joint probabilistic data association with adaptive neuro-fuzzy inference system state filter (CJPDA-ANFISSF) are compared with each other for six different tracking scenarios. It was shown that the tracks estimated by using proposed TST-ANFIS agree better with the true tracks than the tracks predicted by the JPDAF, the TST, the LRA, and the CJPDA-ANFISSF.

A new method based on the multilayered perceptron neural network architecture for computing the wide aperture dimension of the pyramidal horn is presented. The computed wide aperture dimension is used in successfully designing optimum gain pyramidal horn. The other design parameters of the horn are determined from the simple and explicit analytical formulas. These formulas do not need the application of the iterative methods, and are not restricted to the high gain horn designs. The gain of a designed pyramidal horn is determined with no path length error approximation. Better accuracy with respect to the previous design methods is obtained for various pyramidal horn design examples.

Neural models based on multilayered perceptrons for computing the resonant frequency of rectangular microstrip antennas with thin and thick substrates are presented. Eleven learning algorithms, Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, conjugate gradient of Powell-Beale, bayesian regularization, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, resilient backpropagation, conjugate of Polak-Ribiere, backpropagation with adaptive learning rate, one-step secant, and backpropagation with momentum, are used to train the multilayered perceptrons. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by Levenberg-Marquardt algorithm.

This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the input resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based on the least square approach and the backpropagation algorithm is used to optimize the parameters of ANFIS. The input resistance results predicted by ANFIS are in excellent agreement with the experimental results reported elsewhere.

A new method based on artificial neural networks for calculating the narrow aperture dimension of the pyramidal horn is presented. The Levenberg-Marquardt algorithm is used to train the networks. The narrow aperture dimension calculated using artificial neural networks is used in the optimum gain pyramidal horn design. The computed gains of the designed pyramidal horns are in very good agreement with the desired gains.

This paper presents a, new method based on the tabu search algorithm for multiple target tracking. The data, association problem is solved problem formulated as an N-dimensional assignment by using tabu search algorithm. A performance evaluation of the proposed tabu search tracker is presented using simulation studies. Six different tracking scenarios are considered for this evaluation. It, was observed that the estimation results of the tabu search tracker are in very good agreement with the true tracks. Better accuracy with respect to the well known joint probabilistic data association algorithm is obtained.

New expressions for the narrow aperture dimension of the pyramidal horn are presented. They are obtained by using a differential evolution algorithm (DEA), and are used in successfully designing optimum gain pyramidal horns. The DEA is a simple, fast, and robust evolutionary algorithm for solving general type of numeric optimization problems. Design parameters of the horn are determined from the simple and explicit analytical formulas. These formulas do not need the application of the iterative methods, and are not restricted to the high-gain horn designs. The gain of a pyramidal horn is computed without approximating the path length error. Better accuracy with respect to the previous design methods is obtained for various pyramidal horn design examples.

Three formulas for the narrow and wide aperture dimensions of the pyramidal horn are improved, and the improved formulas give lower average percentage errors (0.00452, 0.01566, and 0.00809%), compared to the average percentage errors of three available formulas (0.01497, 0.27561, and 0.07268%). The agreement between the desired gains and the computed gains of the horns designed by using the improved formulas is very good, having average percentage errors of approximately 0.0017–0.0063%. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 239–245, 2003.

A method based on artificial neural networks (ANNs) for calculating the input resistance of circular microstrip patch antennas is presented. The Levenberg–Marquardt algorithm is used to train the networks. The theoretical input resistance results obtained by using this method are in very good agreement with the experimental results available in the literature. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 107–111, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10838

A new, very simple closed-form expression for computing the bandwidth of rectangular microstrip antennas with thick substrates is presented. It is obtained by means of a curve-fitting technique, and is useful for the computer-aided design of microstrip antennas. The theoretical bandwidth results obtained by using this new bandwidth expression are in very good agreement with the experimental results reported elsewhere. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 36: 225–228, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10727