An artificial neural model of the microstrip lines

Türker, Nurhan; Güneş, Filiz

doi:10.1109/siu.2004.1338616

Cited by 18 publications

(41 citation statements)

References 5 publications

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“…But having individual neural model for each performance parameter becomes sometimes unattractive to include in modern antenna computer-aided-design (CAD) programs. It has recently been overcome by introducing 2 ISRN Electronics the concept of generalized neural approach for computing different performance parameters, simultaneously [17][18][19][20]. The resonance frequencies of rectangular, circular, and triangular MSAs have been computed [17][18][19] using generalized neural networks model.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Khan

Де

2014

ISRN Electronics

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Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slotdimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger's substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.

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Section: Introductionmentioning

confidence: 99%

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Khan

Де

2014

ISRN Electronics

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“…ANFIS applications in the field of radio frequency (RF) antenna modeling have been widely applied during the last decade. The resonant frequency computation and synthesis of electrically thin and thick microstrip antennas [10,11], input resistance [12], bandwidth, and resonant frequency computation of various microstrip antennas were studied in the literature [13][14][15].…”

Section: Anfis Applications For Mmpasmentioning

confidence: 99%

Adaptive network-based inference system models on multiband patch antenna design

Demircioğlu¹,

Sazlı²,

Sengul³

et al. 2015

Turk J Elec Eng & Comp Sci

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Abstract:In this study, an adaptive network-based fuzzy inference system (ANFIS) for multiband microstrip patch antenna (MMPA) modeling is proposed. The MMPA includes a single patch with inverted L-shaped stubs on the edges.The antenna production process needs iterative runs of the electromagnetic (EM) simulator, fabricating and testing the simulated antenna to find the optimum geometry. Production processes (design, simulation, fabrication, and testing) and the cost computation time add to the overall manufacturing expenses. A computer-aided modeling approach using the ANFIS is developed and trained with data acquired from the EM simulators, Sonnet Suites and AWR AXIEM, and measurement data. The overall data set includes 7777 input-output pairs, of which 3338 are used for the testing of the ANFIS model. The trained ANFIS model provides accurate and reliable results compared with the EM simulators and measured data.

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“…During the learning process, these synaptic weights could be weakened or strengthened and therefore help the data to be kept in the ANN [5,6]. The benefits, feasibility, and flexibility of ANNs have no formula necessary to design a planar antenna due to the empirical nature, based on the observation of physical phenomena, less computational time as compared with other optimization methods, and compatibility with commercial electromagnetics software [5][6][7][8]. Neural networks can be used for the applications of wireless communications.…”

Section: Introductionmentioning

confidence: 99%

A novel approach of design and analysis of fractal antenna using a neurocomputational method for reconfigurable RF MEMS antenna

Chawla¹,

Khanna²

2016

Turk J Elec Eng & Comp Sci

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Abstract:A mathematical neural approach/artificial neural network (ANN) for the design of a swastika-shaped reconfigurable antenna as a feedforward side is proposed. Further design parameter calculations using the reverse procedure of the above method is presented. Neural network computational is one of the optimization methods that could be considered to improve the performance of the device. In this paper, the proposed planar antenna up to the 2nd iteration is simulated using finite element method-based HFSS software. The developed ANN algorithm method allows the optimization of the antenna to be carried out by exchanging repetitive simulations and also provides reduced processing times while still retaining great accuracy as compared to traditional mathematical formulation. The simulated S-parameter (return loss) results of the proposed antenna are verified with the ANN and show good agreement. Furthermore, for proof of concept, the above proposed antenna as well as a swastika-shaped reconfigurable antenna (2nd iteration) with radio frequency microelectromechanical system switches are fabricated and tested using a vector network analyzer. The results presented here show that the antenna works well in the frequency range of 1.5 to 6.5 GHz and resonates on multiple bands. The novelty of the approach described here offers ease of designing the process using the ANN algorithm, maintaining the miniaturization of antenna size, multiband behavior, and utility of the antenna in the mobile terminal.

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An artificial neural model of the microstrip lines

Cited by 18 publications

References 5 publications

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Adaptive network-based inference system models on multiband patch antenna design

A novel approach of design and analysis of fractal antenna using a neurocomputational method for reconfigurable RF MEMS antenna

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