Resonant frequency evaluation of microstrip antennas using a neural-fuzzy approach

Angiulli, Giovanni; Versaci, Mario

doi:10.1109/tmag.2003.810172

Cited by 37 publications

(46 citation statements)

References 12 publications

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“…The resonant frequencies of MSAs were calculated in [37] by using a neuro-fuzzy network. In [37], the number of rules and the premise parameters of fuzzy inference system (FIS) were determined by the fuzzy subtractive clustering method and then the consequent parameters of each output rule were determined by using linear least squares estimation method.…”

Section: Introductionmentioning

confidence: 99%

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Güney¹,

Sarıkaya²

2008

PIER

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Abstract-A method based on concurrent neuro-fuzzy system (CNFS) is presented to calculate simultaneously the resonant frequencies of the rectangular, circular, and triangular microstrip antennas (MSAs).The CNFS comprises an artificial neural network (ANN) and an adaptive-network-based fuzzy inference system (ANFIS). In a CNFS, neural network assists the fuzzy system continuously (or vice versa) to compute the resonant frequency. The resonant frequency results of CNFS for the rectangular, circular, and triangular MSAs are in very good agreement with the experimental results available in the literature.

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Section: Introductionmentioning

confidence: 99%

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Güney¹,

Sarıkaya²

2008

PIER

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“…Originally introduced by Vapnik and coworkers [22], they are getting more and more popular for overcoming the limitations typical to ANNs (see [23] and references within). This is because the Structural Risk Minimization principle embodied by SVMs has been proved to be more effective than the traditional Empirical Risk Minimization principle employed by ANNs (see [22] and references within), hence equipping the former with a greater ability to generalize, when compared with the latter.…”

Section: Support Vector Regression Machinesmentioning

confidence: 99%

Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators

Angiulli¹,

Carlo²,

Amendola³

et al. 2008

PIER

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Abstract-In this paper an efficient technique for the determination of the resonances of elliptic Substrate Integrated Waveguide (SIW) resonators is presented. The method is based on the implementation of Support Vector Regression Machines trained using a fast algorithm for the computation of the resonant frequencies of SIW structures. Results for resonators with a wide range of parameters will be presented. A comparison with results obtained with Multi Layer Perceptron Artificial Neural Network and with full wave simulations will show the effectiveness of the proposed approach.

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“…The first ones such as method of moments (MoM) [3] on one hand lead to accurate predictions, but they are complex and time consuming specially over wide frequency band, the second ones such as transmission line model (TLM) [4][5][6] on the other hand is simple, but leads to considerably errors in wide frequency band. To remove the above drawbacks, intelligent models such as neural networks (N.N) and neural-fuzzy systems (N.FS) [7][8][9] can be used. It is however well known that these models require too many initial input-output data to create the model and also training process is too long especially when the number of inputs is increased.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Model of the Input Impedance of Rectangular Microstrip Antenna

Ostadzadeh¹

2015

Journal of Fuzzy Set Valued Analysis

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In this paper, a fuzzy-based approach is proposed so as to predict the input impedance of the rectangular microstrip antenna. In the proposed approach, at first, behavior of single microstrip antenna is represented as simple and unchanged membership functions, and the feed probe effect on the input impedance is then extracted as simple curves so that the input impedance of microstrip antenna in despite of other existing models is efficiently predicted.

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Resonant frequency evaluation of microstrip antennas using a neural-fuzzy approach

Cited by 37 publications

References 12 publications

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators

Qualitative Model of the Input Impedance of Rectangular Microstrip Antenna

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