Enhancing the step response curve for rectifier current of HVDC system based on artificial neural network controller

Jabban, Taha; Alali, Mohamad Alaa Eddin; Mansoor, Abdul Kareem Zawba; Hamoodi, Ali Nathem

doi:10.1016/j.jksues.2012.01.002

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Feed-Forward Neural Network is a type of artificial neural network that does not form a cycle [26], [27], [28]. In this Neural Network, data flows in only one direction.…”

Section: A Feed-forward Neural Networkmentioning

confidence: 99%

Parkinson‟s Disease Prediction using Modified Gauss-Newton Method in Feed-Forward Neural Network

2019

IJITEE

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 Abstract: Parkinson's disease (PD) is a brain disorder, characterized by the relapse of the nervous system that spreads gradually in the body. The symptom of PD includes a loss of body control (moderate movement, resting tremors, postural shakiness etc.). So, it is required to detect at an early stage. Machine learning (ML) deals with a variety of probabilistic methods to identify a pattern in a dataset. Therefore, the research is carried out to predict the PD using Multilayer Feed-Forward Neural Network. In Neural Network (NN), weight optimization performed at each layer that plays a major role in the prediction. First-order weight optimization techniques are slow in computation because they reduce the sum of square error using parameter updating in the steepest descent way. In proposed work, a modified recursive Gauss-Newton method is used to optimize the weights for speed up the performance of Feed-Forward NN. This approach is compared with widely used optimization techniques. The Proposed method found better than other techniques and performs fast in Apache Spark than R-Studio framework.

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“…Feed-Forward Neural Network is a type of artificial neural network that does not form a cycle [26], [27], [28]. In this Neural Network, data flows in only one direction.…”

Section: A Feed-forward Neural Networkmentioning

confidence: 99%

Parkinson‟s Disease Prediction using Modified Gauss-Newton Method in Feed-Forward Neural Network

2019

IJITEE

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“…In the past few years, the hybridization of MPPT method and classical PID controller with fuzzy logic control methods are employed for increasing the performance of PID controller without altering the system stability [6]. Huge numbers of papers have been published recently on intelligent controllers such as Artificial Neural Network (ANN) controller, evolutionary algorithms-based controller and fuzzy control [7]. In order to attain the higher power from wind energy, several control methods have been developed here [8,9], such as fuzzy logic control, integrator back stepping, feedback linearization method, neural networks and control of sliding mode.…”

Section: Introductionmentioning

confidence: 99%

A MPPT strategy based on cuckoo search for wind energy conversion system

Kumar

Raglend²

2018

IJET

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The WECS based Doubly Fed Induction Generator (DFIG) system is presented in this paper which includes different MPPT control strategies for a grid connected system. The GSC gives the flow of power from the rotor part of DFIG up to the grid and the modulation of DC voltage. Here the cuckoo search algorithm based on MPPT is designed, to obtain a higher power from the changing speed wind turbine. The algorithms such as Perturb and Observe (P&O), Proportional Integral (PI) control and Fuzzy Logic Controller (FLC) are compared and their performances are evaluated. To design and develop the cuckoo search optimization based on MPPT for WECS, and to obtain optimum voltage regulation and power, thus improving the working performance, reducing the domain time and minimizing the performance indices. To simulate the different MPPT control methods, MATLAB/Simulink environment is used here.

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