Optimization-Oriented Design of RF/Microwave Circuits Using Inverse-Linear-Input Neuro-Fuzzy-Output Space Mapping With Two Different Dimensionality Simulators

Hinojosa, Juan; Pereira, Fernando Daniel Quesada; Martinez-Mendoza, M.; Álvarez‐Melcón, A.

doi:10.1109/tcsi.2010.2055314

Cited by 12 publications

(17 citation statements)

References 37 publications

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“…The ANFIS can simulate and analyse the mapping relation between the input and output data through a learning to determine optimal parameters of a given FIS. Fast and accurate learning, excellent explanation facilities in the form of semantically meaningful fuzzy rules, the ability to accommodate both data and existing expert knowledge about the problem, and good generalization capability features have made neuro‐fuzzy systems popular in recent years [28–42].…”

Section: Adaptive‐network‐based Fuzzy Inference Systemmentioning

confidence: 99%

ANFIS models for synthesis of micro‐coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip

Kaya

Güney

Yildiz

et al. 2011

Micro & Optical Tech Letters

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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.

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Section: Adaptive‐network‐based Fuzzy Inference Systemmentioning

confidence: 99%

ANFIS models for synthesis of micro‐coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip

Kaya

Güney

Yildiz

et al. 2011

Micro & Optical Tech Letters

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“…For the optimization of the selected low‐order 3‐D filter, an IL‐SM procedure is used to find an approximate root of the system of nonlinear equations

X_{3 normalD, i + 1} = P_{1}^{i} ((), {bold-italicX}_{2 D}^{*}) = A^{i} + B^{i} X_{2 normalD}^{*} i = 0, 1, 2, \dots

where

X_{2 normalD}^{*} = ([], X_{2 normalD} X_{3 normalD, z})

, X 3D, z is known, X 2D is obtained from in the first sub‐process and

P_{1}^{i}

is a multidimensional IL vector function ( A i and B i are the coefficient vectors), which is iteratively evaluated through an alignment of the 2‐D and 3‐D EM simulator responses of the selected low‐order filter up to satisfy the error criterion ε

{‖e‖}_{2}^{2} = {(\sqrt{\sum_{i = 1}^{q} {(||, e_{i}^{T})}^{2}})}^{2} \leq ε

where

{‖e‖}_{2}^{2}

is the square of the Euclidean norm of the error vector e , q is the number of discrete frequency points aligning the discrete response specifications between the 2‐D and 3‐D EM simulator responses and e q is the q th error vector given by

e_{q} = R_{2 normalD} ((),, {bold-italicX}_{2 D}, f_{q}) - R_{3 normal...}

…”

Section: Cil‐sm Methodsmentioning

confidence: 99%

“…These strategies use space mapping (SM) optimization techniques in order to reduce the design time . Nevertheless, the first approaches still require a significant amount of computation time and memory to optimize high‐order 3‐D filters , while the last method was introduced for the optimization of 2‐D filters (inductive waveguide structures) .…”

Section: Introductionmentioning

confidence: 99%

Efficient optimization‐oriented design methodology of high‐order 3‐D filters using 2‐D and 3‐D electromagnetic simulators

Hinojosa

Pereira

Bozzi

et al. 2014

Circuit Theory & Apps

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SUMMARYThis paper proposes a computationally highly efficient interface between two-dimensional (2-D) and threedimensional (3-D) electromagnetic (EM) simulators for the optimization-oriented design of high-order 3-D filters. In a first step, the novel optimization-oriented design methodology aligns the 3-D EM simulator response with the 2-D EM simulator response of a low-order 3-D filter by using an inverse linear space mapping optimization technique. Then, a second mapping performs a calibration with the optimal 2-D and 3-D design parameters obtained from the first mapping. The optimization of high-order filters is carried out using only the efficient 2-D EM simulator, and the calibration equations directly give the design parameters of the 3-D filter. The potential and the effectiveness of the proposed optimization-oriented design methodology are demonstrated through the design of C-band 3-D evanescent rectangular waveguide bandpass filters with increasing orders from three to eight.

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“…It is important to remark that the initial dimensions of the isolated cavities shown in Table 2 are used for 2 main purposes. First, they are used to compute the propagation constant for the modes in the different waveguide sections, needed in Equation 13. Second, they are used as initial point for the optimization of the different segments of the structure, as it will be detailed next.…”

Section: Figurementioning

confidence: 99%

“…This high sensitivity makes optimization techniques unreliable, and often find difficulties in the convergence of the final desired response. [10][11][12][13][14] The main drawbacks of these space-mapping optimization methods are the need of 2 models (coarse and fine), and the difficulty to find an initial design when a large number of parameters to be optimized is required. Otherwise, this kind of approaches usually gets trapped into local minima.…”

Section: Introductionmentioning

confidence: 99%

Advanced filter design technique based on equivalent circuits and coupling matrix segmentation

Martínez

Pons-Abenza

Pérez

et al. 2018

Circuit Theory & Apps

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This paper presents a technique for the efficient design of bandpass waveguide microwave filters using a segmentation technique applied to an equivalent circuit. The technique is based on first developing an equivalent circuit that synthesizes the desired transfer function. Then, the different parts of the real physical structure are optimized by segmenting this equivalent circuit. The technique was originally developed for in-line filters, and the main contribution of this paper is in the combination of this technique with the coupling matrix formalism. In this way, we adapt for the first time this design strategy to the design of complex coupling topologies, beyond the in-line configuration. As an example, a complex sixth-order dual-mode filter, implemented in all-inductive waveguide technology, is designed using the new coupling matrix segmentation technique, showing the effectiveness of the presented theory. A prototype of the filter has been manufactured, and the accuracy of the design technique is verified by measurements on the real prototype.

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Optimization-Oriented Design of RF/Microwave Circuits Using Inverse-Linear-Input Neuro-Fuzzy-Output Space Mapping With Two Different Dimensionality Simulators

Cited by 12 publications

References 37 publications

ANFIS models for synthesis of micro‐coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip

ANFIS models for synthesis of micro‐coplanar stripline and asymmetric coplanar stripline with an infinitely wide strip

Efficient optimization‐oriented design methodology of high‐order 3‐D filters using 2‐D and 3‐D electromagnetic simulators

Advanced filter design technique based on equivalent circuits and coupling matrix segmentation

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