Using Neural Networks for Fault Detection in Planar Antenna Arrays

Vakula, D.; Sarma, N. V. S. N.

doi:10.2528/pierl10030401

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Further discussion of this issue is beyond the scope of the paper; in the examples presented here, we used a trial and error process to obtain the optimal solutions.…”

Section: The Optimal Pyramidal Horn Design Criterionmentioning

confidence: 99%

Theoretical Establishment and Evaluation of a Novel Optimal Pyramidal Horn Design Criterion

Baltzis¹

2010

PIER

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Abstract-This paper proposes a novel design criterion for optimal pyramidal horns. According to the criterion, the optimal aperture phase error parameters of a pyramidal horn are determined from the minimization of the horn's lateral surface area. We present two families of curves that illustrate the optimal aperture phase error parameters for frequency and directivity values in the area of practical interest. We also discuss two simple approximate design methods for the calculation of the optimal horn parameters. Comparisons with well-known design methods demonstrate the efficacy of our approach. The designed horns have also smaller aperture area and require less installation space than other optimal proposals in the published literature. Moreover, the proposed criterion produces, under certain conditions, the lightest horn for a given directivity; as a result its fabrication requires less material compared to other structures. The present approach is a useful design tool when the size and weight of a pyramidal horn are of concern.

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Section: The Optimal Pyramidal Horn Design Criterionmentioning

confidence: 99%

Theoretical Establishment and Evaluation of a Novel Optimal Pyramidal Horn Design Criterion

Baltzis¹

2010

PIER

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“…Hence, it requires an intelligent algorithm to locate the fault on branched network from the reflectometry trace. With the development of artificial intelligence technology, artificial neural network (ANN) with strong nonlinear mapping and robust ability has been widely applied to solve the inversion problem and locate fault [7,8]. However, there is no universal method to determine an optimal ANN structure in terms of the number of hidden layers and number of neurons in each layer.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Faulty Cable Network Using Time-Domain Reflectometry

Zhang¹,

Zhang²,

Liu³

2013

PIER

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Abstract-Based on Time-Domain Reflectometry (TDR) technique, a novel method which could locate faults on the coaxial cable distribution network by using Support Vector Machine (SVM) is proposed in this paper. This approach allows the faulty network to be reconstructed by estimating the lengths of branches. A State-transition Matrix model is employed to simulate the TDR response at any port and evaluate the transfer function between two points. SVM is used to solve the inversion problem through training datasets created by the State-transition matrix model. Compared to the existing reflectometry methods, our proposed method can tackle multiple faults in the complex cable networks. Numerical and experimental results pointing out the performance of the SVM model in locating faults are reported.

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“…ANNs are flexible and robust enough to identify the faults in arrays. By using ANNs it is possible to recalculate and produce the radiation pattern close to the original pattern [24][25] [26][29] [31][32]. The adaline [24], multilayer perceptron (MLP) [21][ [25][26][33]the radial basis function (RBF) neural networks [31][32]and the discrete mean field neural network based on Vidyasagar net [22] and have been found quite effective in finding faults in the antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

“…The genetic algorithm is normally used to detect the faulty elements in small size arrays [31] but for large arrays it has to be run several times for accurate results. Applications of radial basis function (RBF) and probabilistic neural network (PNN) have been suggested by authors in [31] and [32] using planar array with 5 x 5 and 8 x 8 isotropic elements having uniform excitations and inter element spacing of 0.5λ. The radiation pattern is sampled between angles -90 0 to 90 0 .…”

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confidence: 99%

“…Fault diagnosis of planar antenna arrays using neural networks [31] Radial basis function and Probabilistic neural networks 5x5 and 8x8 planar arrays 13 Using neural networks for fault detection in planar antenna arrays [32] Radial basis function and Probabilistic neural networks 5x5 and 8x8 planar arrays 14 Element failure diagnosis in a planar antenna array by the use of neural networks [33] Multi layer perceptron neural network 6x 6 planar array, d=0.9λ…”

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confidence: 99%

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