Parameter Identification of Fractional Order Chaotic System via Opposition Based Learning Bare-Bones Imperialist Competition Algorithm

You, Tao; Lei, Dongge; Cai, Lulu; Li, Peijiang

doi:10.2991/ijcis.d.201223.001

Cited by 3 publications

(1 citation statement)

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“…However, as a member of intelligent algorithms, gravitation algorithm has the defects of falling into local extremum and premature convergence. In order to overcome the above shortcomings, Opposition Based Learning (OBL) [18,19] is used to initialize the initial population of GSA to make the distribution of the initial population more uniform. Tent chaos mapping is introduced to improve the diversity of the population and promote the exploration and development of GSA algorithm.…”

Section: Improved Uga For Optimizing Kelmmentioning

confidence: 99%

A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

Yin¹,

Li²,

Laghari³

et al. 2021

ICST Transactions on Scalable Information Systems

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Network intrusion can enter the network through informal channels. Some illegal users utilize Trojans and selfprogrammed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information. Network intrusion seriously harms the security of network information and the legitimate rights of users. Therefore, a bagging strategy-based kernel extreme learning machine for complex network intrusion detection is presented in this paper. This method adopts a bagging strategy to train several sub-kernel extreme learning machines independently. Then the integrated gain of above machines is measured based on the margin distance minimization (MDM) criterion. Selected machines with high gain degree are selected for selective integration to obtain selective integrated learners with strong generalization ability and high efficiency. Then an improved universal gravitation search algorithm is used to optimize the kernel parameters. Meanwhile, a sub-kernel extreme learning machine online update strategy based on incremental learning of batch samples is introduced to realize the online update of intrusion detection model, so that the proposed detection method can effectively be adapted to the changes of complex network environment. Finally, experiments illustrate that the proposed method has better effect on network intrusion detection in terms of detection accuracy and speed, especially for unknown network intrusion connection events, the response speed is fast, the false alarm rate is low.

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Section: Improved Uga For Optimizing Kelmmentioning

confidence: 99%

A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

Yin¹,

Li²,

Laghari³

et al. 2021

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

Improved surrogate-assisted whale optimization algorithm for fractional chaotic systems ’ parameters identification

Wang

Riego

et al. 2022

Engineering Applications of Artificial Intelligence

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Fractional creep model and experimental study of unsaturated silty clay in Fuyang

Chen

Xia

et al. 2023

Front. Earth Sci.

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Due to the long-term overdraft of groundwater in Fuyang, silty clay in this area has been in an unsaturated state for a long time, which caused ground subsidence and threatened the safety of engineering construction. Creep is an important part of ground subsidence, but it is easily ignored in ground subsidence caused by groundwater overdraft. Therefore, in order to explore the creep behavior in this environment, a series of triaxial creep tests of unsaturated soil were conducted to research the effects of deviator stress, matrix suction and net confining pressure on creep. Then, based on analysis results of the geometric characteristics of the test creep curves, an improved fractional order Nishihara model of unsaturated soil under triaxial stress conditions was constructed by using fractional calculus theory. Finally, the effectiveness of improved fractional order Nishihara model was verified based on cooperation search algorithm and minimum mean square error principle. The simulation results show that the fitting curves of improved fractional order Nishihara model are in good agreement with the test curves, and it is feasible and effective to describe the creep characteristics of unsaturated silty clay in Fuyang.

show abstract

Parameter Identification of Fractional Order Chaotic System via Opposition Based Learning Bare-Bones Imperialist Competition Algorithm

Cited by 3 publications

References 0 publications

A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

Improved surrogate-assisted whale optimization algorithm for fractional chaotic systems ’ parameters identification

Fractional creep model and experimental study of unsaturated silty clay in Fuyang

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