Low RCS Dipole Array Synthesis Based on Mom-Pso Hybrid Algorithm

Wang, Wentao; Gong, Shuxi; Zhang, Yujie; Zha, Feng-Tao; Jin, Lu; Wan, Tingting T.

doi:10.2528/pier09060902

Cited by 27 publications

(15 citation statements)

References 18 publications

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“…As is known, normal global optimization techniques conduct only one search operation in one iteration. For example, the PSO carries out a global search at the beginning stage and local search in the ending stage [20,21], nevertheless, the ABC features in the following advantage in that it conducts both a global search and local search in each iteration, and as a result the probability of finding the optimal is significantly increased, which effectively avoids local optima to a large extent.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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“…The PSO algorithm is a metaheuristic optimization method of complex systems [16]. It was firstly proposed in 1995 by Kennedy and Eberhart [17] at the international conference on neural networks.…”

Section: Domain Geometry and Propertymentioning

confidence: 99%

An Emi Inversing Problem for Landmine Characterization Based on Improved Particle Swarm Optimization and Finite Element Analysis

Matriche¹,

Féliachi²,

Zaoui³

et al. 2013

PIER B

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Abstract-This paper discusses the characterization of landmine by using the electromagnetic induction technique (EMI). The proposed approach is based on the identification of the physical and geometrical properties of a landmine, from the sensor response. But in such an identification, the inverse problem is unavoidable. At first, we begin by simulating the landmine signature by solving a direct problem using the finite element method which constitutes the direct model. After that, we determine the landmine characteristics by using an inverse model based on a cost function optimization. This model is based on an iterative process which coupling finite element analysis and Particles Swarm Optimization (PSO). In this step, we apply two PSO techniques: the Standard PSO (SPSO) and the Improved PSO (IPSO), and discuss the problem of local minima of the cost function. The proposed iterative model is applied to determine the conductivity, geometry, and depth of metallic landmine from its signature measured by EMI. The numerical solution gives good results for the identification of landmine.

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“…It has been widely used for many military applications such as UWB radars, military high data rate wireless communications, etc. But with the development of target identification technology, UWB antennas with low RCS become crucial to the survivability of the antenna platforms [11][12][13][14][15]. However, few publications on the RCS reduction of UWB antennas have been published.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ultra-Wide Band Antenna With Reduced Radar Cross Section

Hong¹,

Gong²,

Jiang³

et al. 2009

PIER

Self Cite

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Abstract-A novel printed dual circular ring monopole antenna with low radar cross section (RCS) for ultra-wide band (UWB) application is proposed. The proposed antenna is designed to realize RCS reduction. The two circular rings are connected by several metallic via holes to improve the radiation characteristics of the proposed antenna. Its UWB-related characteristics are simulated and experimentally verified. The RCS performance of the proposed antenna under different loads is studied and compared with that of a commonly used circulardisc monopole antenna. The results show that the proposed antenna has good radiation performances and lower RCS than the reference antenna. The proposed antenna serves as a good candidate in the design of UWB antenna with the requirement of RCS control.

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Low RCS Dipole Array Synthesis Based on Mom-Pso Hybrid Algorithm

Cited by 27 publications

References 18 publications

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

An Emi Inversing Problem for Landmine Characterization Based on Improved Particle Swarm Optimization and Finite Element Analysis

A Novel Ultra-Wide Band Antenna With Reduced Radar Cross Section

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