.png){kind=icon}
返回
Echo state network optimization using binary grey wolf algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2019.12.069.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities to solve nonlinear problems. It is able to map low-dimensional input signals to high-dimensional space for information extraction, but it is found that not every dimension of the reservoir output directly contributes to the model generalization. This work aims to improve the generalization capabilities of the ESN model by reducing the redundant reservoir output features. A novel hybrid model, namely binary grey wolf echo state network (BGWO-ESN), is proposed which optimises the ESN output connection by the feature selection scheme. Specially, the feature selection scheme of BGWO is developed to improve the ESN output connection structure. The proposed method is evaluated using synthetic and financial data sets. Experimental results demonstrate that the proposed BGWO-ESN model is more effective than other benchmarks, and obtains the lowest generalization error. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Echo state network
Binary grey wolf optimization
Time series
Network structure optimization
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
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏The association of chemotherapy induced neutropenia on treatment outcomes in small cell lung cancer.png)
Lung Cancer
Lung CancerIF0
PREAI
分享收藏Binary grey wolf optimization approaches for feature selection基于二进制灰狼优化的特征选择方法NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A novel photovoltaic power forecasting model based on echo state network基于回声状态网络的光伏发电功率预测模型NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容