Concentric Circular Antenna Array Synthesis Using Comprehensive Learning Particle Swarm Optimizer

Elsaidy, Elsayed; Dessouky, Moawad I.; Khamis, S.; Albagory, Yasser

doi:10.2528/pierl11112506

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…A large sample leads to a large number of input nodes for the neural network. A large neural network is hard to implement and requires a lot of data and time for training [8,9,15]. In order to reduce the number of nodes and to learn the desired magnitude of the array factor at the same time avoid overfitting, validation or early stopping is often used.…”

Section: Methodsmentioning

confidence: 99%

“…etc.) and the corresponding output data (8). The objective of training is to modify the biases and weights of the network to minimise the error function and to finish with desired output for all possible values of input feed.…”

Section: Figure 2 Biological Neuronmentioning

confidence: 99%

“…To result the final output y, the sum is passed on to a nonlinear filter known activation function which releases the output y. y = f (I) ( 8 ) where f can be a simple threshold function or a sigmoidal, hyperbolic tangent or radial basis function. In this work, we used Lavenberg-Marquard feed forward back propagation algorithm to train and simulate the network.…”

Section: Figure 2 Biological Neuronmentioning

confidence: 99%

“…But, artificial neural network (ANN) have been studied in various application like pattern recognition systems, and have been exploited for inputoutput mapping, for system identification, for adaptive prediction, etc.... Therefore, we are interested to present in this paper the neural networks method that will be applied to the array pattern synthesis, highlighting their most important features and distinctive characters [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm

Hamdi¹,

Selma²,

Aguili³

2016

PIER B

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Abstract-Recently, researchers were interested in neural algorithms for optimization problems for several communication systems. This paper shows a novel algorithm based on neural technique presented to enhance the performance analysis of beam-forming in smart antenna technology using N elements for Uniform Circular Array (UCA) and Concentric Circular Array (CCA) geometries. To demonstrate the effectiveness and reliability of the proposed approach, simulation results are carried out in MATLAB. The radiators are considered isotropic, and hence mutual coupling effects are ignored. The proposed array shows a considerable improvement against the existing structures in terms of 3-D scanning, size, directivity, HPBW and SLL reduction. The results show that multilayer feed-forward neural networks are robust and can solve complex antenna problems. However, artificial neural network (ANN) is able to generate very fast the results of synthesis by using generalization with early stopping method. Important gain in the running time and memory used is obtained using this latter method for improving generalization (called early stopping). To validate this work, several examples are shown.

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Section: Methodsmentioning

confidence: 99%

Section: Figure 2 Biological Neuronmentioning

confidence: 99%

Section: Figure 2 Biological Neuronmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm

Hamdi¹,

Selma²,

Aguili³

2016

PIER B

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“…Various optimization methods have been reported to synthesize sparse circular arrays in [14][15][16][17][18][19][20][21][22], including analytical techniques and evolutionary algorithms. In [14] and [15], the vector mapping (VM) method and matrix mapping method were used to handle complicated constraints for linear and planar arrays, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Constraint Synthesis of Rotationally Symmetric Sparse Circular Arrays Using a Hybrid Algorithm

Wang¹,

Jiao²

2019

PIER M

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Rotationally symmetric sparse circular arrays are synthesized under multiple constraints. By combining the modified differential evolution algorithm based on the harmony search (in short HSDE) with the vector mapping (VM) method, a hybrid algorithm, called VM-HSDE, is proposed for synthesizing sparse circular arrays with low sidelobe levels. Due to the array's specific geometry, the number of optimization variables is reduced, and the constrained optimization problem is simplified. Moreover, infeasible solutions are avoided, and the problem is effectively solved by the VM-HSDE algorithm. Finally, three pattern optimization results verify the effectiveness and reliability of the VM-HSDE algorithm.

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PSO in Concentric Circular Arrays for Side Lobe Reduction with Symmetric Relocation Boundary Condition

Chakravorty

Mandal

2015

Intelligent Computing and Applications

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Concentric Circular Antenna Array Synthesis Using Comprehensive Learning Particle Swarm Optimizer

Cited by 17 publications

References 16 publications

Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm

Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm

Multiple-Constraint Synthesis of Rotationally Symmetric Sparse Circular Arrays Using a Hybrid Algorithm

PSO in Concentric Circular Arrays for Side Lobe Reduction with Symmetric Relocation Boundary Condition

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