A Robust Fuzzy-Logic Technique for Computer-Aided Diagnosis of Microwave Filters

Miraftab, V.; Mansour, R.R.

doi:10.1109/tmtt.2003.820895

Cited by 57 publications

(25 citation statements)

References 12 publications

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“…The total mesh number for R f is 1080. The first step is the optimization of the coarse model, which is performed using the procedure similar to (9)- (11). The approximate optimum of the coarse model, x (0) = [9.41 9.47 9.39 1.81 2.29 2.35 0.10 0.102 0.217] T mm, is obtained at the cost of only 65 coarse model evaluations.…”

Section: Microstrip Hairpin Filter [29]mentioning

confidence: 99%

“…The SBO techniques employed in microwave engineering are either based on approximation models (such as neural networks [4][5][6], support-vector regression [7][8][9], fuzzy systems [10][11][12], Cauchy approximation [13]), or physicallybased surrogates (such as space mapping [1,[14][15][16][17] or simulation-based tuning [18][19][20]). Methods exploiting approximation models are quite versatile, but a large set of training samples is normally necessary to create the surrogate, which requires a substantial computational effort.…”

Section: Introductionmentioning

confidence: 99%

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Low-Cost Parameter Extraction and Surrogate Optimization for Space Mapping Design Using Em-Based Coarse Models

Koziel

Leifsson

2011

PIER B

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Abstract-Space mapping (SM) is one of the most popular surrogatebased optimization techniques in microwave engineering. The most critical component in SM is the low-fidelity (or coarse) modela physically-based representation of the structure being optimized (high-fidelity or fine model), typically evaluated using CPU-intensive electromagnetic (EM) simulation. The coarse model should be fast and reasonably accurate. A popular choice for the coarse models are equivalent circuits, which are computationally cheap, but not always accurate, and in many cases even not available, limiting the practical range of applications of SM. Relatively accurate coarse models that are available for all structures can be obtained through coarselydiscretized EM simulations. Unfortunately, such models are typically computationally too expensive to be efficiently used in SM algorithms. Here, a study of SM algorithms with coarsely-discretized EM coarse models is presented. More specifically, novel and efficient parameter extraction and surrogate optimization schemes are proposed that make the use of coarsely-discretized EM models feasible for SM algorithms. Robustness of our approach is demonstrated through the design of three microstrip filters and one double annular ring antenna.

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Section: Microstrip Hairpin Filter [29]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Cost Parameter Extraction and Surrogate Optimization for Space Mapping Design Using Em-Based Coarse Models

Koziel

Leifsson

2011

PIER B

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“…The authors in [11] proposed the algorithm based on fuzzy logic system (FLS) that can be used in tuning of microwave filters. In their concept they used FLS to generate the entries of filter coupling matrix for given scattering parameters at the sampling frequencies.…”

Section: Introductionmentioning

confidence: 99%

On Linear Mapping of Filter Characteristic to Position of Tuning Elements in Filter Tuning Algorithm

Michalski¹

2012

PIER

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Abstract-This work presents a novel approach in building a multidimensional approximator which is used as a linear operator for mapping the vector of detuned filter characteristic to the vector of deviations of tuning elements. This has been done for the purpose of using it in postproduction filter tuning algorithm. With the use of collected sets of deviations of tuning elements and filter characteristics corresponding to them, the least squares method (LSM) is applied to determine the matrix which realizes the linear mapping between these vectors. The matrix found in this method approximates the vectors of both spaces (filter characteristics and corresponding deviations of tuning elements). In tuning process this matrix is used to determine the vector of tuning element deviations for a given detuned filter characteristic read from Vector Network Analyzer. To increase the "quality" of linear operator filter characteristics are transformed with the use of Karhunen-Loeve transform (Principal Component Analysis). In contrast to non-linear artificial intelligence approximators used in filter tuning and published to-date, this method does not require a time-consuming training process. Filter tuning experiments have been performed and proved the correctness of the presented approach.

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“…The ability of soft computing techniques in modeling complicated problems in a vanishingly short time instead of using numerical or analytical approach [25][26][27][28][29][30][31][32] may provide a fast and accurate solution to this problem. Among the soft computing techniques the ability of fuzzy inference method in solving complicated electromagnetic problems such as microwave filter tuning [33,34], EMC problems [35], resonant frequency computation [36,37], determination of the transmission lines characteristic parameters [38], determination of the relative magnetic permeability [39] and also antenna modeling [40][41][42] has been proved in several publications. Artificial neural network (ANN) which is also a well-known soft computing technique has been used recently in microwave filter design [43].…”

Section: Introductionmentioning

confidence: 99%

Active Learning Method for the Determination of Coupling Factor and External Q in Microstrip Filter Design

Rezaee¹,

Tayarani²,

Knöchel³

2011

PIER

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Abstract-In the final step of any filter design process, the desired center frequency, coupling factor and external quality factor (Q ext ) are used to determine the physical parameters of the filter. Although in the most cases the physical dimensions of a single resonator for a given center frequency are determined using exact analytical or simple approximate equations, usually such simple equations cannot be found to easily relate the required coupling factor and Q ext to the physical parameters of the filter. Analytical calculation of coupling factor and Q ext versus dimensions are usually complicated due to the geometrical complexities or in some cases such as microstrip resonators due to the lack of exact solution for the field distribution. Therefore coupling factor and Q ext of various kinds of resonators, especially microstrip resonators, are related to the physical parameters of the structure by the use of time consuming full wave simulations. In this paper a surprisingly fast and completely general approach based on a soft computing pattern-based processing technique, called active learning method (ALM) is proposed to overcome the time consuming process of coupling factor and Q ext determination. At first the ALM technique and the steps of modeling are generally described, then as an example and in order to show the ability of the method this modeling approach is implemented to model the coupling factor and Q ext surfaces of microstrip open-loop resonators versus physical parameters of the structure. Using the ALM-based extracted surfaces for coupling factor and Q ext , two four pole Chebychev bandpass filters are designed and fabricated. Good agreement between the measured and simulated results validates the accuracy of the proposed approach.

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A Robust Fuzzy-Logic Technique for Computer-Aided Diagnosis of Microwave Filters

Cited by 57 publications

References 12 publications

Low-Cost Parameter Extraction and Surrogate Optimization for Space Mapping Design Using Em-Based Coarse Models

Low-Cost Parameter Extraction and Surrogate Optimization for Space Mapping Design Using Em-Based Coarse Models

On Linear Mapping of Filter Characteristic to Position of Tuning Elements in Filter Tuning Algorithm

Active Learning Method for the Determination of Coupling Factor and External Q in Microstrip Filter Design

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