A Back Propagation Neural Network with Double Learning Rate for PID Controller in Phase-Shifted Full-Bridge Soft-switching Power Supply

2020 ◽  
Vol 15 (6) ◽  
pp. 2811-2822
Author(s):  
Yan-Ming Cheng ◽  
Cheng Liu ◽  
Jing Wu ◽  
He-Miao Liu ◽  
Il-Kyoo Lee ◽  
...  
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.


2014 ◽  
Vol 536-537 ◽  
pp. 1493-1496
Author(s):  
Zhu Lei Shao

Aiming at the application requirements of miniaturization and energy saving in the field of high voltage power supply, a high voltage switching power supply with soft switching technology was designed. The soft switch technology which makes the loss of switch power supply decrease obviously,and the dual voltage rectifier technology can effectively reduce the volume of switching power supply. Circuit parameters of switching power supply are optimized in order to further improve the efficiency of power supply. From the experiment results, the high voltage switching power supply meets the design requirements, and suitable for the strict requirements of environment on the volume and efficiency.


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