Intrusion Detection Based on Self-adaptive Differential Evolution Extreme Learning Machine with Gaussian Kernel

2017 ◽  
pp. 13-24 ◽  
Author(s):  
Junhua Ku ◽  
Bing Zheng
Keyword(s):  
Intrusion Detection ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Gaussian Kernel ◽  
Adaptive Differential Evolution ◽  
Learning Machine ◽  
Self Adaptive

scholarly journals Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

2019 ◽  
Vol 27 (1(133)) ◽  
pp. 67-77 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Xu Gao ◽  
Zefei Zhu ◽  
Xudong Hu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Dictionary Learning ◽  
Gabor Filter ◽  
Differential Evolution Algorithm ◽  
Adaptive Mutation ◽  
Multi Scale ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Learning Machine

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine
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