Abstract
The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.
Highlights
Since proposed by Huang et al, extreme learning machine (ELM) has shown fast training speed and incomparable classification ability in the fields of machine learning and neural networks during the past decades [1, 2]
Several experiments are provided in diverse ways to demonstrate the effectiveness of the proposed HI-deep kernel incremental extreme learning machine (DKIELM) approach
To verify the effectiveness and robustness of the HI-DKIELM algorithm, the experiment is divided into five parts: 1. First of all, in section “Evaluation of the performance of the hybrid coyote optimization Beetle Swarm Optimization (HCOBSO) optimizationalgorithm using the CEC2017 and CEC2019 database”, the CEC2017 and CEC 2019 database is adopted to test the performance and robustness of the HCOBSO optimization algorithm proposed in section “The proposed HCOBSO method” and compared with other incomplete optimization methods to test the contribution of each improvement
Summary
Since proposed by Huang et al, extreme learning machine (ELM) has shown fast training speed and incomparable classification ability in the fields of machine learning and neural networks during the past decades [1, 2]. The hybrid intelligent optimization method was adopted to modify the parameters of a deep kernel incremental extreme learning machine (DKIELM) and improve the training speed and classification accuracy.
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