scholarly journals Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 177
Author(s):  
Mateusz Zychlewicz ◽  
Radoslaw Stanislawski ◽  
Marcin Kaminski
Keyword(s):  
Neural Network ◽  
Grey Wolf Optimizer ◽  
State Variables ◽  
Grey Wolf ◽  
Neural Controller ◽  
Mass System ◽  
Numerical Tests ◽  
Speed Controller ◽  
The Neural Network ◽  
Oscillation Damping

In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally.

Modal amplitude and phase estimation of multimode near field patterns based on artificial neural network with the help of grey-wolf-optimizer

2021 ◽  
Vol 67 ◽  
pp. 102720
Author(s):  
Naoto Sugawara ◽  
Takeshi Fujisawa ◽  
Kodai Nakamura ◽  
Yusuke Sawada ◽  
Takayoshi Mori ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Field ◽  
Grey Wolf Optimizer ◽  
Phase Estimation ◽  
Grey Wolf ◽  
Field Patterns ◽  
Modal Amplitude ◽  
Artificial Neural

scholarly journals Investigation of a Multitasking System for Automatic Ship Berthing in Marine Practice Based on an Integrated Neural Controller

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1167
Author(s):  
Van Suong Nguyen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coordinate System ◽  
Numerical Simulations ◽  
Neural Controller ◽  
The Neural Network ◽  
Artificial Neural ◽  
Ship Berthing

In this article, a multitasking system is investigated for automatic ship berthing in marine practices, based on artificial neural networks (ANNs). First, a neural network with separate structures in hidden layers is developed, based on a head-up coordinate system. This network is trained once with the berthing data of a ship in an original port to conduct berthing tasks in different ports. Then, on the basis of the developed network, an integrated mechanism including three negative signs is linked to achieve an integrated neural controller. This controller can bring the ship to a berth on each side of the ship in different ports. The whole system has the ability to berth for different tasks without retraining the neural network. Finally, to validate the effectiveness of the proposed system for automatic ship berthing, numerical simulations were performed for berthing tasks, such as different ports, and berthing each side of the ship. The results indicate that the proposed system shows a good performance in automatic ship berthing.

scholarly journals A Physiological Neural Controller of a Muscle Fiber Oculomotor Plant in Horizontal Monkey Saccades

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Alireza Ghahari ◽  
John D. Enderle
Keyword(s):  
Neural Network ◽  
Muscle Fiber ◽  
Dominant Factor ◽  
Optimal Control Strategy ◽  
Abducens Nucleus ◽  
Neural Controller ◽  
Antagonist Muscle ◽  
The Neural Network ◽  
Time Optimal ◽  
Agonist And Antagonist

A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude.

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