Array pattern synthesis using neural networks with mutual coupling effect

Shavit, R.; Taig, I.

doi:10.1049/ip-map:20045121

Cited by 12 publications

(6 citation statements)

References 7 publications

(11 reference statements)

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“…Eventually, all the bees (solutions) will be drawn to this location since they will not be able to find any other better location. This represent a convergence of the algorithm and the optimum solution is obtained [11,13,17,[20][21][22][23][24]. The main steps of the PSO algorithm are given below and will be elaborated in the remaining portion of this section.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

On Chebyshev Array Design Using Particle Swarm Optimization

Bataineh¹

2011

JEMAA

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In this paper, the evolutionary algorithm of particle swarm optimization (PSO) is applied to synthesis an optimal linear array in the Chebychev sense. Equiripple radiation patterns may be obtained by synthesizing the excitation currents feeding the array or by carefully choosing the interelement spacing. The desired equal side lobes level is achieved simultaneously with the narrowest possible beamwidth (high directivity). Though the optimization problem may become nonlinear, convex and/or sometimes nonconvex, it can be handled using an efficient, a robust and a nongradient based particle swarm optimizer algorithm. In order to effectively utilize this algorithm it is important to define an appropriate objective, or cost, function that return a single number to enable the PSO algorithm minimizing it. In this paper, the objective function is formulated taking into consideration the level of the side lobes as well as the main beam width. In addition to satisfy the objective function, the obtained results using the proposed technique are in agreement with those available in the literature

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

“…The position and velocity of each particle are updated according to some fitness function [11]. Some studies have been devoted to compare between the GA and PSO [20,21] and a general conclusion has been reached the PSO shows better performance due to its greater implementation simplicity and minor computational time.…”

Section: Introductionmentioning

confidence: 99%

On Chebyshev Array Design Using Particle Swarm Optimization

Bataineh¹

2011

JEMAA

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“…The thinning and synthesis of pencil beam pattern with minimum sidelobe based on PSO have been discussed in [12,13]. Some studies have been devoted to compare between the GA and PSO [14,15] and a general conclusion has been reached. The PSO shows better performance due to its greater implementation simplicity and minor computational time.…”

Section: Introductionmentioning

confidence: 99%

Concentric Circular Antenna Array Synthesis Using Comprehensive Learning Particle Swarm Optimizer

Elsaidy¹,

Dessouky²,

Khamis³

et al. 2012

PIER Letters

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Abstract-Concentric circular antenna array (CCAA) is synthesized to generate pencil beam with minimum side lobe level (SLL). The comprehensive learning particle swarm optimizer (CLPSO) is used for synthesizing a ten-ring CCAA with central element. This Synthesis is done by finding the optimum current excitation weights and interelement spacing of rings. The computational results show that sidelobe level is reduced to −40.5 dB with narrow beamwith about 4.1 • .

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“…The evolutionary algorithms (EAs) for array synthesis have been extensively studied. Several global optimization algorithms such as differential evolution (DE) [1,7], genetic algorithm (GA) [4,6], simulated annealing (SA) [8], ant colony optimization (ACO) [9], particle swarm optimization (PSO) [10][11][12][13][14] are used in antenna array pattern. However, these methods present certain drawbacks with the possibility of premature convergence to a local optimum.…”

Section: Introductionmentioning

confidence: 99%

Application of Chaotic Particle Swarm Optimization Algorithm to Pattern Synthesis of Antenna Arrays

Wang¹,

Feng²,

Liu³

2011

PIER

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Abstract-To deal with pattern synthesis of antenna arrays, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively. To evaluate the performance of CPSO, several representative benchmark functions are minimized using various optimization algorithms. Numerical results demonstrate that the proposed approach improves the performance of the algorithm significantly, in terms of both the convergence speed and exploration ability. Moreover, CPSO was applied to array synthesis examples, including the equally spaced linear array, unequally spaced linear array and conformal array, compared with other optimization methods. Experimental results show its high performance in the pattern synthesis with low side lobe, multi-nulls and shaped beam.

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Array pattern synthesis using neural networks with mutual coupling effect

Cited by 12 publications

References 7 publications

On Chebyshev Array Design Using Particle Swarm Optimization

On Chebyshev Array Design Using Particle Swarm Optimization

Concentric Circular Antenna Array Synthesis Using Comprehensive Learning Particle Swarm Optimizer

Application of Chaotic Particle Swarm Optimization Algorithm to Pattern Synthesis of Antenna Arrays

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