Gradient-based adjoint-variable optimization of broadband microstrip antennas with mixed-order prism macroelements

Karatzidis, Dimitrios I.; Yioultsis, Traianos V.; Tsiboukis, T.D.

doi:10.1016/j.aeue.2007.05.011

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…Starting from a reasonably good design, which can often be found using approximate formulas, direct optimizers can fine-tune the device toward a nearby local minimum in relatively few CFEs (e.g.,[10][11][12][13][14][15][16][17][18][19][20]. This is especially true if the sensitivity of the cost function with respect to the design parameters is available cheaply, as it is using adjoint methods (Lee et al, 1995;Lee & Itoh, 1997;Nair & Webb, 2003;Bondeson et al, 2004;Karatzidis et al, 2008). This is the type of optimization considered in this article.…”

Section: Introductionmentioning

confidence: 95%

“…The optimization of microwave devices using the frequency-domain finite element (FE) method is well established (Lee et al, 1995;Lee & Itoh, 1997;Nair & Webb, 2003;Bondeson et al, 2004;Monge et al, 2005;Wang et al, 2005;Karatzidis et al, 2008). The cost function to be minimized depends on the electromagnetic field within the device; thus, each cost function evaluation (CFE) requires an FE analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Optimization of Microwave Devices over a Frequency Band

Nair¹,

Webb²

2010

Electromagnetics

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Traditional optimization using the finite element method keeps the accuracy of the finite element analysis roughly constant throughout the optimization. Adaptive optimization uses adaptive finite element analysis and increases the accuracy as the optimization proceeds, since a coarse solution is adequate early on, but more accuracy is needed as the optimum is approached. Here, adaptive optimization is extended to cost functions that depend on the performance of the device over a range of frequencies, which changes the criterion used to terminate the adaption. Results are presented for rectangular waveguide components: an E-plane bend, an impedance transformer, and a magic-T.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Adaptive Optimization of Microwave Devices over a Frequency Band

Nair¹,

Webb²

2010

Electromagnetics

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“…E. Saenz, K. Guven, E. Ozbay, I. Ederra, and R. Gonzalo have treated in [4] decoupling of multifrequency dipole antenna arrays for microwave imaging applications. Recently, many multiagent stochastic optimization techniques that incorporate random variation and selection, such as evolutional programming(EPs) [6]-7], genetic algorithms (GA) [8]- [9], particle swarm optimization (PSO) [10]- [11], artificial neural network (ANN) [12]- [13], and gradient-based techniques [14]- [15], have been implemented via computer codes. The Vivaldi antenna has been used in many microwave engineering applications for its simple structure, low cross polarization, and highly directive patterns characteristics, especially in ultra bandwidth applications such as vehicular wireless communication, radar imaging, and through-the-wall imaging [16]- [17].…”

Section: Introductionmentioning

confidence: 99%

Taguchi-RBF Neural networks Based Optimization of Phased Array Antenna With Coupling Effects

Oureghi

Ghayoula

Amara

et al. 2023

AEM

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In the antenna array synthesis problems, most of the works in literature utilize isotropic elements. Thus, the mutual coupling effects between the array elements are neglected. It is obvious that an array antenna synthesized by neglecting the coupling effects cannot be used in the real world applications due to the possible mismatch between the desired and realized radiation patterns. In this paper, a novel method based on neural network algorithm RBF (Radial Basis Function ) for the synthesis and model of Antipodal Vivaldi antenna with mutual coupling effect is presented. The synthesis in implementation’s method for this type of array permits to approach the appropriated radiation pattern while considering the mutual coupling between its elements. The neural network is used to estimate the array elements’ excitations. The architecture of the neural network based on the radial basis functions (RBFs) is introduced and simulation results are presented. Results show that there is an agreement between the desired specifications and the synthesized one. The proposed optimization approach offers an efficient and robust synthesis procedure.

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“…Recently, many multiagent stochastic optimization techniques that incorporate random variation and selection, such as evolutional programming (EPs) [1,2], genetic algorithms (GA) [3,4], particle swarm optimization (PSO) [5,6], artificial neural network (ANN) [7,8], and gradient-based techniques [9,10], have been implemented via computer codes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 12.5 GHz Microstrip Antenna Array Using Taguchi's Method

Spasos

Nilavalan

Tsiakmakis

et al. 2011

International Journal of Antennas and Propagation

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This paper presents Taguchi's optimization method implemented in the design of a single feed (without any matching network) microstrip antenna array operating around 12.5 GHz. The proposed optimization method is statistical and is widely used for quality assurance in many fields such as mechanical and chemical production, consumer electronics, services; however it has been underused in the field of electromagnetics. It allows optimization of multiparameter, multitarget complex designs in a very short time in conjunction with advanced simulation tools. The proposed antenna has been fully evaluated under Taghuchi's and PSO's optimization methods, and the experimental results show total Gain of 15 dB, and good matching withS11 better than 20 dB, in the frequency range 12.3 to 12.8 GHz.

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Gradient-based adjoint-variable optimization of broadband microstrip antennas with mixed-order prism macroelements

Cited by 10 publications

References 20 publications

Adaptive Optimization of Microwave Devices over a Frequency Band

Adaptive Optimization of Microwave Devices over a Frequency Band

Taguchi-RBF Neural networks Based Optimization of Phased Array Antenna With Coupling Effects

Optimization of a 12.5 GHz Microstrip Antenna Array Using Taguchi's Method

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