Neural network based on improved parallel bat algorithm and its application

被引：0
|
作者：
Zhao, Zhuo-Qiang [1 ]
Liu, Shi-Jian [2 ]
Xu, Lin [3 ]
Pan, Jeng-Shyang [1 ,4 ]
机构：
[1] Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou,350118, China
[2] Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou,350118, China
[3] STEM University of South Australia, Australia
[4] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao,266590, China
来源：
Journal of Network Intelligence | 2021年 / 6卷 / 03期
关键词：
Back propagation neural networks - Bat algorithms - BP neural networks - Communication strategy - ITS applications - Local minimums - PID controllers - Slow convergences;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The artificial neural network is a research hotspot emerging in the field of artificial intelligence. Back Propagation (BP) neural network plays an important role in neural network. The network has the disadvantages of slow convergence of learning algorithms and easy falling into a local minimum. In this paper, a parallel bat algorithm with a new communication strategy is proposed, which is used to optimize the weights and thresholds of the BP neural network and establish the improved PBA-BP model. It was applied to optimize the parameters of the PID controller. Finally, we verified the effectiveness of the proposed method through the simulations. © 2021, Taiwan Ubiquitous Information. All rights reserved.
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页码：428 / 439
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