Direction of Arrival and State of Polarization Estimation Using Radial Basis Function Neural Network (Rbfnn)

Zainud-Deen, S.H.; Awadalla, K. H.; El-Hadad, E. S.

doi:10.2528/pierb07111801

Cited by 18 publications

(13 citation statements)

References 20 publications

(18 reference statements)

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“…ANN is one of the most popular and widely applied methods due to its superior ability of approximating unknown nonlinear function to any degree of desired accuracy. This method has been widely applied to many fields, such as image processing, pattern recognition, signal processing, and weather prediction, particularly in the area of the electromagnetics [24-26, [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Torkaman¹,

Afjei²

2010

PIER

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Abstract-In this paper, a new hybrid method of obtaining the degrees of freedom for redial airgap length in Switched Reluctance Motor operation under normal and faulty conditions based on magnetostatic analysis is presented. At the beginning, this method goes through the magnetic design of the motor utilizing three dimensional (3-D) Finite Element Method (FEM) in order to consider the end effects as well as axial fringing field effects. The motor parameters, such as torque, flux linkage, flux density versus rotor position are precisely obtained. Then, a Multi Layered Perception Neural Network is designed by considering the nonlinear behavior of the motor parameters obtained under different modes of operation. Using this network and the obtained parameters from FEM, an Objective Function (OF) for torque ripple with the aim of having a minimum mean square error is estimated. In addition, an improved Genetic Algorithm (GA) for the minimization the OF is also presented to determine the motor's operational regions. Finally, the legal intervals for different modes of motor operation are addressed.

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Section: Introductionmentioning

confidence: 99%

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Torkaman¹,

Afjei²

2010

PIER

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“…In the past decades, adaptive beamforming has been widely used in radar [4,5], direction finding (DF) [6][7][8], wireless communications [9], medical imaging [10], and other areas [11,12]. However, the adaptive beamformers are much more sensitive to the steering vector errors, which will degrade the performance of the adaptive beamformers severely [13,14].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming for a Class of Gaussian Steering Vector Mismatch

Gu¹,

Shi²,

Chen³

et al. 2008

PIER

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Abstract-Compared with the worst-case optimization-based approach, the probability-constrained approach is a more flexible one to robust adaptive beamforming. In this paper, a precise relationship between the two approaches is built in the case of Gaussian steering vector mismatch, which shows that the probability-constrained beamformer design can be interpreted in terms of the worst-case beamformer design. Numerical simulations demonstrate that the precise version of the probability-constrained beamformer is more robust to the steering vector mismatch than the other popular robust adaptive beamformers.

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“…Ability and adaptability to learn, generalisability, fast real-time operation, and ease of implementation have made NNs popular for a number of microwave design problems in recent years [2]. Citing just a number of examples: NNs have been used in the design of passive microwave circuits [3], analysis and synthesis of microstrip lines [4], calculation of the characteristic impedance of air-suspended trapezoidal and rectangular-shaped microshield lines [5], design of nonlinear microwave circuits based on active devices [6], design of microstrip patch antennas [7,8], direction of arrival estimation with antenna arrays [9,10], radar target recognition [11], aperture antenna shape prediction [12], inverse scattering of dielectric cylinders [13], near field to far filed transformation [14], and synthesis of antenna array [15,16]. In addition to modeling the response, NNs can also be employed within the core of the full-wave solvers based on the method of moments [17,18].…”

Section: Introductionmentioning

confidence: 99%

Neural Frequency Sweeper for Accelerating S-Parameters Calculation of Planar Microwave Structures

Soliman¹,

Ibrahim²

2008

PIER M

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Abstract-This paper presents a new frequency-sweep approach for the efficient calculation of S-parameters of planar microwave structures. The approach is based on approximating the frequency dependence of the real and imaginary parts of the S-parameters using neural networks. Due to its superior performance, radial basis functions neural network (RBF-NN) is adopted. A limited number of frequency samples are used to train the RBF-NN. Then, the trained RBF-NN is capable of providing a smooth frequency response with very high accuracy in a fraction of a second. The proposed method is applied to a number of planar microwave structures such as: Patch antenna with an inset feed, band-rejection filter, and branch-line coupler. According to the presented results, a speed factor of at least 10 is measured, and a maximum percentage error of 3.29% is recorded.

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Direction of Arrival and State of Polarization Estimation Using Radial Basis Function Neural Network (Rbfnn)

Cited by 18 publications

References 20 publications

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Robust Adaptive Beamforming for a Class of Gaussian Steering Vector Mismatch

Neural Frequency Sweeper for Accelerating S-Parameters Calculation of Planar Microwave Structures

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