.png){kind=icon}
返回
Dynamic Quaternion Extreme Learning Machine
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2021.3067014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization-based Q-ELM (QEM-ELM) that only needs to optimize the output weights of the newly added neuron is developed in this brief. On this basis, a dynamic network construction scheme is further extended on Q-ELM, leading to a novel DQ-ELM, where the hidden nodes can be dynamically recruited or deleted according to the significance to network performance. The network parameters can be optimized and the architecture can be self-adapted simultaneously. Simulation results on many benchmark datasets demonstrate that the proposed QEM-ELM and DQ-ELM achieve good generalization performance by preserving a compact network size.
Keyword:
Quaternions
Neurons
Algebra
Training
Optimization
Computer architecture
Circuits and systems
Quaternion algebra
quaternion EM-ELM
dynamic quaternion ELM
quaternion GHR
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
4.9
论文数:
8.8K
被引数:
2.5W
机构
引用论文
分享收藏Quaternion-valued short-term joint forecasting of three-dimensional wind and atmospheric parameters.png)
RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏
Feed forward neural network with random quaternionic neurons具有随机四元数神经元的前馈神经网络SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏分享收藏
查看更多内容