摘要
随机配置网络(SCN)通过引入不等式约束来限制输入权重和偏置的赋值,随着节点数量增加,网络能够逼近任意的数学函数和数据模型。在构建SCN的过程中,由于网络本身性质以及样本数据的不适定性和病态条件等问题会引起网络的过拟合,故提出一种基于Dropout技术的改进型SCN模型(Dropout-SCN)来自适应地约束输出权重分布和大小,以此来提高网络模型的识别精度。光纤数据验证的结果表明:与传统的SCN和L2范数正则化的SCN模型相比,Dropout-SCN模型具有更低的测试误差,有效地减缓了网络过拟合问题,提高了对光纤预警系统(OFPS)中光纤入侵信号的识别准确率。
A stochastic configuration network (SCN) introduces inequality constraints to limit the assignment of input weights and biases. The network can approximate arbitrary mathematical function and data model as the number of hidden nodes gradually increases. In the process of SCN construction, the properties of the network itself and the ill-posed and ill-conditioned problems of the sample data may cause over-fitting of the network model. This study proposes an improved SCN model based on the Dropout technology, called Dropout-SCN, to improve the recognition accuracy of the network model by adaptively constraining the output weight distribution. We then perform a verification using optical fiber data. Compared with the traditional SCN and L2 norm regularized SCN models, the Dropout-SCN model has a lower test error, which effectively prevents the network over-fitting problem and improves the recognition accuracy of the intrusion signals in the optical fiber pre-warning system.
作者
盛智勇
曾志强
曲洪权
李伟
Sheng Zhiyong;Zeng Zhiqiang;Qu Hongquan;Li Wei(School of Electronic Information Engineering,North China University of Technology,Beijing 100144,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第14期39-46,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61571014,61601006)
北京自然科学基金(4172017)
关键词
光通信
随机配置网络
L2正则化
Dropout技术
光纤预警系统
信号处理
optical communications
stochastic configuration network
L2 regularization
Dropout technology
optical fiber pre-warning system
signal processing
