Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models

Alhmoud, Lina; Dairy, Abdul Raouf Al; Faris, Hossam; Aljarah, Ibrahim

doi:10.3390/sym13061079

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Hexaferrites and hexaplana ferrites are two different forms of hexagonal ferrites having anisotropic magnetism. Hexaferrites have a typical composition of MFe12O19, where 'M' is a divalent metal cation such as Ba, Sr, or Pb [24,44,45]. While both hexaferrite and spinel ferrite have a similar crystal structure, with densely packed oxygen ions, the primary distinction is that certain layers in hexaferrite include metal ions with the same ionic radius as the oxygen ions.…”

Section: Crystal Structure Of Hexaferritesmentioning

confidence: 99%

An Overview of Hard Ferrites: Types and Structures

Jasrotia

2023

Materials Research Foundations

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In 1950s, after the discovery of hexagonal ferrites called as hexaferrites, there has been an increasing level of curiosity in hexaferrites and still, it is increasing exponentially. Commercially and technologically, hexaferrites belong to a category of important magnetic materials due to their utilization in various applications such as permanent magnets, recording media, storage devices, rod antennas, high frequency devices, microwave devices etc. There are six types of hexagonal ferrites as M-type, Z-type, Y-type, X-type, U-type and W-type respectively, out of which, the most worldwide manufactured hexagonal ferrite is Barium M-type hexagonal ferrite named as BaM [1]. All categories of hexaferrites shows ferrimagnetic behaviour as their magnetic nature is inherently related to their crystal structure. The current chapter focuses on the classification and structure of hexagonal ferrites. In this book chapter, firstly, we report the six categories of hexaferrites as M, Y, W, X, Z, W and then, we provide a detailed comprehensive review of two approaches called as spinel and S/R/T based model for reporting the structure of hexagonal ferrites respectively. After that, we reported a comprehensive structure of each hexaferrite. Lastly, the concluding remarks have been presented in the current chapter.

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Section: Crystal Structure Of Hexaferritesmentioning

confidence: 99%

An Overview of Hard Ferrites: Types and Structures

Jasrotia

2023

Materials Research Foundations

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“…The advantage of NEAT is that it allows neural networks to evolve both their weights and topology, without having to heuristically specify the desired network topology. Neuroevolutionary algorithms for signal/data processing are being explored in several branches of applied sciences, such as materials science [30], gaming [31] and cloud computing [32].…”

Section: Literature Review and Motivationmentioning

confidence: 99%

Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone

Mariani

Fiori

2023

Aerospace

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The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter’s current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function’s definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times.

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Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models

Cited by 2 publications

References 32 publications

An Overview of Hard Ferrites: Types and Structures

An Overview of Hard Ferrites: Types and Structures

Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone

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