Online Hybrid Kernel Learning Machine with Dynamic Forgetting Mechanism

被引：0

作者：

Wang, Yuhua ^{[1
,2
]}

Li, Deyu ^{[1
]}

Xu, Yuezhu ^{[1
]}

Wang, Hao ^{[1
]}

机构：

[1] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Peoples R China

[2] Modeling & Emulat E Govt Natl Engn Lab, Harbin, Peoples R China

来源：

EMERGING NETWORKING ARCHITECTURE AND TECHNOLOGIES, ICENAT 2022 | 2023年 / 1696卷

基金：

国家重点研发计划;

关键词：

Online learning; Forgetting mechanism; Noise-reducing autoencoder; Machine learning; Intrusion detection; Industry-oriented control system;

D O I：

10.1007/978-981-19-9697-9_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper, for the purpose of meeting challenges of fewer resources of storage and calculation in the detection of ICS intrusion as well as real-time requirements, has particularly designed an online hybrid kernel learning machine with dynamic forgetting mechanism. First, on the basis of online kernel limit learning machine, a dynamic forgetting mechanism is designed to dynamically adjust the amount of forgetting data according to the current block error, which reduces the system burden and improves the detection accuracy. Then, it replaces the former single kernel function with a hybrid kernel function, which successfully advances the accuracy rate and generalized performance. Finally, a hybrid noise-reducing autoencoder is created to perform dimensional reduction of industrial data with huge dimensions, resulting in the improvement of algorithm and efficiency. The validity and superiority of the proposed online hybrid kernel learning machine with dynamic forgetting mechanism are verified through simulation experiments.

引用

页码：273 / 285

页数：13