Electromagnetic-artificial neural network model for synthesis of physical dimensions for multilayer asymmetric coupled transmission structures (invited article)

Watson, Paul; Cho, Choonsik; Gupta, K.C.

doi:10.1002/(sici)1099-047x(199905)9:3<175::aid-mmce4>3.0.co;2-p

Cited by 27 publications

(7 citation statements)

References 13 publications

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“…That is to say, when the input signal approaches the center of base function, the hidden layer node will has a large output. Gauss function is one of frequently used base functions [2] [3] [4] :…”

Section: Artificial Neural Network (Ann) Modeling and Optimizedadesignmentioning

confidence: 99%

Neural network model of optical fiber direction coupler design

Bao

2004

Optical Modeling and Performance Predictions

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The coupling length and coupling ratio is defined as input and output respectively, which are used to train the neural network model. Radial base function neural network model is built for optical fiber direction coupler, which is in turn simulated and designed through the model. This method has the advantages of speediness, accuracy and reliability, which are identified by the design example. It can be used to design the other kind of optical passive device. This way is a novel design approach, which can save the cost of device design, decrease period of design and has a good prospect of application.

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Section: Artificial Neural Network (Ann) Modeling and Optimizedadesignmentioning

confidence: 99%

Neural network model of optical fiber direction coupler design

Bao

2004

Optical Modeling and Performance Predictions

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“…In this article, the extended version of the (WCIP) method, developed in Ref. [] to study arbitrary shaped scatterers, is applied in the analysis of the scattering problem by a set of perfect conducting angular structures using the artificial neural network (ANN) . The supervised learning with the multilayer feed‐forward network architecture is chosen together with The resilient backpropagation known as RPROP algorithm .…”

Section: Introductionmentioning

confidence: 99%

“…[7] to study arbitrary shaped scatterers, is applied in the analysis of the scattering problem by a set of perfect conducting angular structures using the artificial neural network (ANN). [19][20][21][22][23] The supervised learning with the multilayer feed-forward network architecture is chosen together with The resilient backpropagation known as RPROP algorithm. [24][25][26][27] The main aim of this algorithm is to identify the appropriate electromagnetic coupling operator between each two pixels among all pixels of the discretized surface and to optimize the calculation time for a large huge mesh surface.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for electromagnetic scattering by a set of perfect conducting cylindrical objects using the artificial neural network

Ines¹,

Ammar²,

Aguili³

et al. 2018

Int J RF Microw Comput Aided Eng

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In this article an efficient algorithm based on the wave concept iterative process is formulated and applied in order to investigate the electromagnetic scattering by a set of conducting arbitrarily shaped objects placed in the free space. In this approach, the cylindrical coordinates system is used as a modal base in order to develop the modal coefficients of the diffraction operator. This operator models the reaction of the environment expressing the electromagnetic coupling between each two pixels of the discretized surface. A study of electromagnetic coupling between two pixels positioned and oriented somehow in the free space is highlighted so as to determine the coupling operator for different positions and orientations of the emitted and received waves. Twelve coupling operators are developed. For a complex geometric shape structure, the determination of these characteristics involved the determination of interactions between each pixel and the others that constitute the discretized surface. This interaction involves the wave formulation already highlighted for each couple of pixels which implies a higher calculation time. In order to optimize the calculation time, the artificial neural networks are adopted with the feed‐forward architecture. The supervised learning has been chosen with the use of the resilient backpropagation algorithm.

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“…Neural network techniques have been used for a wide variety of microwave applications such as transmission line components [5], [8], vias [31], bends [32], coplanar waveguide (CPW) components [33], spiral inductors [7], field effect transistor (FET) devices [25], [34], heterojuction bipolar transistor (HBT) devices [35], high electron mobility transistor (HEMT) devices [36], [37], filters [38]- [41], amplifiers [42]- [45], mixers [46], antennas [47], embedded passives [4], [26], [27], packaging and interconnects [48], etc. Neural networks have also been used in circuit simulation and optimization [3], [25], [49], signal integrity analysis and optimization of very-large-scale-integration (VLSI) interconnects [48], [50], microstrip circuit design [51], process design [52], microwave impedance matching [53], inverse modeling [54], measurements [55], synthesis [25], [56] and behavioral modeling of nonlinear RF/microwave subsystems [57].…”

Section: Outline Of the Thesismentioning

confidence: 99%

Advanced Neural-Based Model Generation and Extrapolation Techniques for Microwave Applications

Na¹

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Neural-based modeling techniques have been recognized as important vehicles in the microwave computer-aided design (CAD) area in addressing the growing challenges of designing next generation microwave device, circuits and systems. Artificial neural network models can be trained to learn electromagnetic (EM) and physics behavior. The trained neural network models can be used in high-level circuit and system design allowing faster simulation and optimization including EM and physics effects in components. The purpose of this thesis is to develop advanced neural-based model generation and extrapolation techniques for microwave applications. The proposed techniques take advantage of the high-efficiency of automated model generation algorithm, the cost-effective concept of knowledge-based neural network and the generalization capability of extrapolation techniques, to achieve First of all, I would like to express my sincere appreciation to my supervisor, Prof. Q. J. Zhang, for his professional guidance, invaluable inspiration and continuous support throughout the research work and the preparation of this thesis. This thesis would not have been completed without his patience, assistance, encouragement and faith of this study. It was my great honor to work under his supervision and guidance. Special appreciation to my co-supervisor, Prof. Jianguo Ma for the invaluable counsel and knowledgeable instruction. His invaluable counsel provided a great help for me on my way pursuing my Ph.D degree.

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Electromagnetic-artificial neural network model for synthesis of physical dimensions for multilayer asymmetric coupled transmission structures (invited article)

Cited by 27 publications

References 13 publications

Neural network model of optical fiber direction coupler design

Neural network model of optical fiber direction coupler design

An efficient algorithm for electromagnetic scattering by a set of perfect conducting cylindrical objects using the artificial neural network

Advanced Neural-Based Model Generation and Extrapolation Techniques for Microwave Applications

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