Hybrid Genetic Based Algorithm for CNN Ultra Compression

Abotaleb, Abdelrhman M.; Elwakil, Ahmed Tarek; Hadhoud, Mayada

doi:10.1109/icm48031.2019.9021521

Cited by 5 publications

(1 citation statement)

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“…The basic task of the mentioned algorithms is the selection of network weighting coefficients. However, there are also applications in which the structure of the model [28][29][30] or the initial values of the network parameters are optimized [31]. Regularization is also widely used for neural networks to overcome overfitting [32][33][34].…”

Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

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Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

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