Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ahmad M. Zaki"'
Publikováno v:
Ain Shams Engineering Journal, Vol 9, Iss 1, Pp 65-75 (2018)
This paper presents a proposed approach based on an adaptive fuzzy logic controller for precise control of the DC motor speed. In this concern, the proposed Direct Adaptive Fuzzy Logic Controller (DAFLC) is estimated from two levels, where the lower
Externí odkaz:
https://doaj.org/article/359fe0ceacbb41b288625d08b5f26e90
Publikováno v:
Neural Computing and Applications. 34:22367-22386
In the present paper, a hybrid deep learning diagonal recurrent neural network controller (HDL-DRNNC) is proposed for nonlinear systems. The proposed HDL-DRNNC structure consists of a diagonal recurrent neural network (DRNN), whose initial values can
Publikováno v:
International Journal of Control. :1-14
Publikováno v:
Neural Computing and Applications. 33:1515-1531
For the current paper, the technique of feed-forward neural network deep learning controller (FFNNDLC) for the nonlinear systems is proposed. The FFNNDLC combines the features of the multilayer feed-forward neural network (FFNN) and restricted Boltzm
Publikováno v:
Arabian Journal for Science and Engineering. 41:3727-3737
The present paper is a trial to shed further light on the Indirect Adaptive Fuzzy Logic Controller (IDAFLC). In this concern, the proposed technique is predestined from two levels, where the lower level is based on Mamdani fuzzy controller. On the ot
Publikováno v:
Australasian Medical Journal. :23-27
Background: ICD/BVP indications are expanding. They areexpensive devices and historically, morbidities associated withtheir use were high. The starting experience at the Gold CoastHospital is being reviewed.Methods: A retrospective chart review of al
Publikováno v:
Ain Shams Engineering Journal, Vol 9, Iss 1, Pp 65-75 (2018)
This paper presents a proposed approach based on an adaptive fuzzy logic controller for precise control of the DC motor speed. In this concern, the proposed Direct Adaptive Fuzzy Logic Controller (DAFLC) is estimated from two levels, where the lower