Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks

Şimşek, Murat; Aoad, Ashrf

doi:10.1007/978-3-319-27517-8_8

Cited by 7 publications

(7 citation statements)

References 16 publications

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Section: Introductionmentioning

confidence: 99%

“…There had been many studies for providing numerical and analytical methods for high accurate electromagnetic models for design of microwave devices, one of the extensively used methods is artificial neural network (ANN) models. [35][36][37] ANN can simply be defined as a numerical model of human brain where it is aimed to train the network to predict the linear or nonlinear relationships between given inputs and outputs. Usually an ANN model for a microwave design is created either by using measured or simulated characteristics such as scattering parameters, reflection phase, characteristics impedance, resonant frequencies, or any dimensional design parameter such as length or width, and so on although the process of gathering training and test data for ANN modeling is a considerable effort, once an ANN model is obtained the total computation time of simulation or prediction can be overlooked, especially in case of repeated design analysis or design optimization process.…”

Section: Introductionmentioning

confidence: 99%

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Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model

Mahoutı

2019

Int J RF Microw Comput Aided Eng

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In this work, design optimization of a varicap diode loaded antenna consisting of four identical rectangular microstrips is presented as a pattern reconfigurable antenna at 5.2 GHz. The microstrips are printed on the front of a FR4 substrate with the dimensions of 40 mm × 25 mm and ε r = 4.6, h = 1.58 mm and probe‐fed via a coupling using a rectangular microstrip line symmetrically placed between them. In first stage, S11 of the antenna are obtained as its real and imaginary parts as continuous functions of geometry of the microstrip components within 3 to 7 GHz using multi‐layer perceptron (MLP) trained and validated by 3D EM simulated data. In order to determine the most suitable (MLP) architecture and training algorithm, 20 different MLP architectures are tested. Then, S11 are optimized with respect to the geometry parameters using differential evolution algorithm and MLP based model. The antenna is prototyped with the optimally selected parameters and measured. From the comparison of simulation and measurement results, it can be observed that the measurement results agree with the simulation results, thus it can be concluded that the proposed antenna is a simple and successful design subject to the design purposes with together its design methodology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model

Mahoutı

2019

Int J RF Microw Comput Aided Eng

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“…In such a case, the designer should either use a low accurate coarse model for computationally efficient optimization process or a highly accurate fine design model with the low computational efficient optimization process. For the last decades, many studies have been done on creating numerical or analytical methods for accurate models for the design of microwave stages, one of the most commonly used numerical methods is artificial neural network (ANN) models . Commonly, either measured or simulated results of microwave designs are being used for creating ANN‐based circuit models.…”

Section: Introductionmentioning

confidence: 99%

“…For the last decades, many studies have been done on creating numerical or analytical methods for accurate models for the design of microwave stages, one of the most commonly used numerical methods is artificial neural network (ANN) models. [7][8][9][10] Commonly, either measured or simulated results of microwave designs are being used for creating ANN-based circuit models. Even though the gathering of training and test data might become a considerable effort, once the ANN model is created the overall computation duration of estimation can be overlooked, most especially during a design optimization process.…”

Section: Introductionmentioning

confidence: 99%

Deep learning base modified MLP model for precise scattering parameter prediction of capacitive feed antenna

Çalık

Belen

Mahoutı

2019

Int J Numerical Modelling

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The relations between the antennas' geometrical parameters and design specifications usually consist of linear and nonlinear components. Especially with the increase of the requested performance measures, the design procedure becomes much more complex due to the conflicting performance criteria or design limitations. To achieve a design with high performance with feasible design parameters, a fast, accurate, and reliable design optimization process is required. Herein, to have a fast, accurate, and high‐performance capacitive‐feed antenna model to be used in design optimization problems, a modified multi‐layer perceptron (M2LP) model has been proposed. The M2LP is an equivalent convolutional neural network (CNN) model of a standard multilayer perceptron (MLP), where instead of traditional training parameters of MLP, more advanced training parameters of CNN models such as batch‐norm layer, leaky‐rectified linear unit (ReLU) layer, and Adam training algorithm had been used. Furthermore, the M2LP model had been used in a design optimization process and the obtained optimal antenna had been prototyped using 3D printing technology for justification of the proposed M2LP model with experimental results. As can be seen from the results, the proposed M2LP model is a fast, accurate, and reliable regression model for design optimization of microwave antennas.

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“…Simsek applied knowledge-based modelling to the engineering modelling of reconfigurable five finger microstrip patch antennas. Knowledge-based ANNs were used to obtain more accurate results and required less time consumption and even less training data through the coarse model efficiency [13]. Simsek also developed a three-step modelling strategy that exploits knowledge-based techniques to improve some properties of conventional ANN modelling such as accuracy and data requirement [14].…”

Section: Introductionmentioning

confidence: 99%

Base types selection of PSS based on a priori algorithm and knowledge‐based ANN

Zhang

Chai

Liu

et al. 2019

IET Collaborative Intelligent Manufacturing

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Manufacturers tend to bundle a product with its related services as a product service system (PSS), to create more values for customers and gain competitive advantages for themselves. Configuration design is the key process of PSS development. Configuring a PSS involves selecting and combining appropriate product and service components, to satisfy individual customer requirements. This study studies the mapping relationship between customer requirement attributes and PSS base types in PSS for CNC machine tools, which provides a great reference value for engineers in configuration design. Owing to the high complexity and non‐linearity between customer requirement and product, an integrated intelligent learning method based on a priori algorithm and knowledge‐based artificial neural network (ANN) is proposed in this study. First, the data of historical configuration instance data sets are processed and then a priori algorithm is used to extract the effective rules as domain knowledge. Domain knowledge is used to build the initial structure of ANN. Moreover, data sets are used to further optimize the network. The knowledge‐based ANN is used to realize the mapping between customer requirement attributes and PSS base types. The proposed method is validated in the selection of the PSS base type for CNC machine tools.

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Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks

Cited by 7 publications

References 16 publications

Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model

Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model

Deep learning base modified MLP model for precise scattering parameter prediction of capacitive feed antenna

Base types selection of PSS based on a priori algorithm and knowledge‐based ANN

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