scholarly journals Online learning based neural network adaptive controller for efficient power tracking of PWR type reactor with unknown internal dynamics

2022 ◽  
Vol 168 ◽  
pp. 108866
Author(s):  
V Govindan ◽  
N Pappa
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Adaptive Controller ◽  
Internal Dynamics ◽  
Type Reactor ◽  
Efficient Power

Analysis of a neural-network-based adaptive controller for deep-space formation flying

2021 ◽  
Author(s):  
Chihang Yang ◽  
Hao Zhang ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Formation Flying ◽  
Adaptive Controller ◽  
Deep Space

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

2017 ◽  
Vol 88 (10) ◽  
pp. 105001 ◽  
Author(s):  
Liang Yan ◽  
Lu Zhang ◽  
Bo Zhu ◽  
Jingying Zhang ◽  
Zongxia Jiao
Keyword(s):  
Neural Network ◽  
Pso Algorithm ◽  
Adaptive Controller

A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application

2019 ◽  
Vol 66 (5) ◽  
pp. 1794-1804 ◽  
Author(s):  
Donghyeon Han ◽  
Jinsu Lee ◽  
Jinmook Lee ◽  
Hoi-Jun Yoo
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Low Power ◽  
Object Tracking ◽  
Real Time ◽  
Deep Neural Network

scholarly journals Architecture of Artificial Neural Network in Identification of Internal Dynamics and Prediction of Dynamic System Rainfall Data Time Series

2012 ◽  
pp. 34-42
Author(s):  
Sanjeev Karmakar ◽  
Manoj Kumar Kowar ◽  
Pulak Guhathakurta
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Dynamic System ◽  
Long Range ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Global Minima ◽  
Internal Dynamics ◽  
Artificial Neural

The objective of this study is to expand and evaluate the back-propagation artificial neural network (BPANN) and to apply in the identification of internal dynamics of very high dynamic system such long-range total rainfall data time series. This objective is considered via comprehensive review of literature (1978-2011). It is found that, detail of discussion concerning the architecture of ANN for the same is rarely visible in the literature; however various applications of ANN are available. The detail architecture of BPANN with its parameters, i.e., learning rate, number of hidden layers, number of neurons in hidden layers, number of input vectors in input layer, initial and optimized weights etc., designed learning algorithm, observations of local and global minima, and results have been discussed. It is observed that obtaining global minima is almost complicated and always a temporal nervousness. However, achievement of global minima for the period of the training has been discussed. It is found that, the application of the BPANN on identification for internal dynamics and prediction for the long-range total annual rainfall has produced good results. The results are explained through the strong association between rainfall predictors i.e., climate parameter (independent parameter) and total annual rainfall (dependent parameter) are presented in this paper as well.

scholarly journals Loss minimization DTC electric motor drive system based on adaptive ANN strategy

2020 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Sim Sy Yi ◽  
Wahyu Mulyo Utomo ◽  
Goh Hui Hwang ◽  
Chien Siong Kai ◽  
Alvin John Lim Meng Siang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Learning ◽  
Electric Motor ◽  
Drive System ◽  
Motor Drive ◽  
Loss Minimization ◽  
Light Load ◽  
Flux Level ◽  
Electric Motor Drive

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies.

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