Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Dhaliwal, Balwinder Singh; Pattnaik, Shyam S.

doi:10.1155/2013/560969

Cited by 13 publications

(10 citation statements)

References 36 publications

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“…The performance of three different ANN models using same set of 58 samples is evaluated in terms of average absolute error [36]. This parameter indicates the quality of training.…”

Section: Ann Analysis Of Proposed Gcsamentioning

confidence: 99%

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Ann and Fa Based Design of Hybrid Fractal Antenna for Ism Band Applications

Kaur¹,

Sivia²

2020

PIER C

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In this paper, a compact Giuseppe Peano, Cantor Set, and Sierpinski Carpet fractals based hybrid fractal antenna (GCSA) is designed and developed for Industrial, Scientific and Medical (ISM) band applications. The proposed GCSA is a hybrid fractal design which is created by fusing Giuseppe Peano, Cantor set, and Sierpinski carpet fractals together. The optimization of the microstrip line feed position is performed by using a Firefly Algorithm (FA). The substrate material employed for proposed GCSA is a low-priced, easily available FR4 epoxy of thickness 1.6 mm. By varying the geometrical dimensions of the radiating patch, a data set of 58 GCSAs is randomly generated for the realization of Artificial Neural Network (ANN) and FA approaches. The designed structure is fabricated, and then measured results are evaluated. The proposed GCSA is capable of resonating at 2.4450 GHz with S(1, 1) < −10 dB. The measured bandwidth of the operating ISM band is 101 MHz. The quantitative performance of three different ANN types reveals that Feed Forward Back Propagation ANN (FFBPN) shows minimum error in comparison to other two ANN types. The simulated, experimental, and optimized results show a good match that specifies the preciseness of the measurement.

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“…The performance of three different ANN models using same set of 58 samples is evaluated in terms of average absolute error [36]. This parameter indicates the quality of training.…”

Section: Ann Analysis Of Proposed Gcsamentioning

confidence: 99%

“…Each of the testing and validation subsets consists of 9 samples. In FFBPN type of ANN, there are two neurons at the input layer, ten neurons at the hidden layer, and one neuron at the output layer [36]. The employed training function is trainlm and the learning Table 4.…”

Section: Ann Analysis Of Proposed Gcsamentioning

confidence: 99%

Ann and Fa Based Design of Hybrid Fractal Antenna for Ism Band Applications

Kaur¹,

Sivia²

2020

PIER C

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“…The ANN is basically an efficient information processing model that is similar to a biological neural network in terms of its functional characteristics. ANNs play an important role in fractal antenna analysis, as the mathematical models of such antennas are too difficult to implement . ANNs possess several highly interconnected processing units called neurons.…”

Section: Ann Modelmentioning

confidence: 99%

Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna for biomedical applications using artificial neural network and firefly algorithm

Kaur

Sivia

2019

Int J RF Microw Comput Aided Eng

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In this research paper, Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna (GCHFA) is proposed that operates for biomedical applications. The proposed GCHFA is designed by merging Giuseppe Peano and Cantor set fractals that help in achieving better performance characteristics as well as miniaturization. Firefly algorithm (FA) has been employed to optimize the feed position of the designed antenna. The substrate material selected for the proposed GCHFA is low-cost, commercially available FR4 epoxy whose thickness is 1.6 mm and relative permittivity is 4.4. A data set of 65 GCHFAs with different geometrical parameters is generated for the realization of two different bioinspired approaches.For the performance evaluation of fabricated prototype, vector network analyzer is used. The experimentally observed resonant frequencies are 2.4400 and 5.8115 GHz, and at these resonant frequencies, S (1,1) < −10 dB. The designed antenna is suitable for Industrial, Scientific, and Medical bands of biomedical applications. Moreover, the behavior of the proposed GCHFA is nearly omnidirectional. A comparative study of three different artificial neural networks (ANNs) is also done to evaluate the most suitable ANN type for the analysis of proposed GCHFA. The optimized, simulated, and experimental results depict that they are closely matched. K E Y W O R D S artificial neural network, biomedical applications, feed position, firefly algorithm, hybrid fractal antenna, resonant frequency

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“…The use of ANN for modeling the resonant frequencies of fractal antennas is at initial stage as only a few applications of ANN in fractal antenna domain are found in literature. The frequency of operation of Sierpinski gasket fractal antenna is modeled using different types of ANN by Dhaliwal and Pattnaik . ANN and particle swarm optimization (PSO)–based hybrid technique is proposed by Anuradha et al to design Koch monopole and Sierpinski gasket fractal antennas.…”

Section: Introductionmentioning

confidence: 99%

“…The successful application of ANN modeling in predicting different parameters of microstrip antennas is an inspiring factor to apply the ANN model for complex fractal antenna design. The capabilities of ANN models are further enhanced by using ensembling approach .…”

Section: Introductionmentioning

confidence: 99%

Modeling of circular fractal antenna using BFO‐PSO–based selective ANN ensemble

Pattnaik

Dhaliwal

2019

Int J Numerical Modelling

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Accurate design of miniaturized antenna is constrained by the limited wellformulated exact mathematical expressions. Demands for smart devices with features like portability, implantability, and configurability have further placed bigger challenges in front of the antenna design engineers or scientists. As a part of the search for various solutions, many innovative approaches have been proposed by various authors in different literatures. Application of soft computing is also another design approach to accurate design of fractal antenna. Here, the authors have attempted to propose a better solution to miniaturized antenna and its design. A fractal antenna based on circular outer geometry has been proposed as a solution to the search of miniaturized antennas, and a particle swarm optimization-based selective artificial neural networks ensemble is developed, which is employed as the objective function of a bacterial foraging optimization algorithm leading to a hybridized algorithm. The developed hybrid algorithm is utilized to develop the proposed antenna at 2.45 GHz. A good agreement of the simulated, desired, and experimental results validates the proposed design approach.KEYWORDS ANN ensemble, BFO, fractal antenna, ISM band, PSO | INTRODUCTIONFractal geometry is employed by many researchers to design the miniaturized antennas suitable for compact handheld wireless devices, MIMO applications, rectennas, and implantable devices. 1-5 The requirement for smart and intelligent systems made it imperative to look forward to design methods capable of high precision antenna and antenna systems. The system integration approach further demands for miniaturization of antenna with enhanced parameter capability to meet the demands of bandwidth, gain, directivity, pattern shape, front to back lobe ratio, main to side lobe ratio, noise rejection, and many others. In the present context, fractal antenna due to its properties like self-similar design to maximize the electrical length appears to be a candid choice. The fractal geometry has often features like fine structure at arbitrarily small scale, too irregular to be easily described in Euclidean geometry, self-similar, and has a Hausdorff dimension that is greater than the topological dimension which leads to space filling curves such as Hilbert curve, has a simple and recursive definition, etc; therefore, in the last few years, many researchers are working on fractal antenna to provide many innovative solution.Complex fractal shapes are used for the design of fractal patch antennas. Due to nonexistence of the closed-form exact mathematical formulas for fractal antennas, the design and analysis of these antennas are a challenging task. The analytical methods are suitable for simple microstrip antennas, but derivation of analytical methods is very difficult for fractal

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Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Cited by 13 publications

References 36 publications

Ann and Fa Based Design of Hybrid Fractal Antenna for Ism Band Applications

Ann and Fa Based Design of Hybrid Fractal Antenna for Ism Band Applications

Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna for biomedical applications using artificial neural network and firefly algorithm

Modeling of circular fractal antenna using BFO‐PSO–based selective ANN ensemble

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