Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Wu, Di; Qu, Zong; Guo, Feng; Zhu, Xiao Lin; Wan, Qing

doi:10.1080/00051144.2019.1570642

Cited by 10 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, a random population with n chromosomes is created. [20][21][22]. ELM can be considered as an algorithm which offers a fairly effective learning speed.…”

Section: Genetic Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

Avcı¹,

Sert²

2021

View full text Add to dashboard Cite

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

show abstract

“…As a result, a random population with n chromosomes is created. [20][21][22]. ELM can be considered as an algorithm which offers a fairly effective learning speed.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Figure 2. The architecture of SHLFF with ELM ELM, Single Hidden Layer FeedForward (SHLFF) is a method proposed for ANN training[20][21][22]. ELM can be considered as an algorithm which offers a fairly effective learning speed.…”

mentioning

confidence: 99%

An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

Avcı¹,

Sert²

2021

View full text Add to dashboard Cite

show abstract

“…In this part, we compare the HCOBSO algorithm with four baseline optimization methods: COA, BSO, ICOA, and DE-MPGWO [15] using typical functions.…”

Section: Performance Evaluation Of the Hcobso Optimization Algorithm On Typical Functionsmentioning

confidence: 99%

“…Traditional particle swarm optimization algorithm has low searching speed despite its local optimization merits. Therefore, a hybrid optimization approach called DEPSO based on differential evolution algorithm and the particle swarm optimization method is given in [15] to optimize the hidden layer nodes. A novel hybrid optimization method given in [16] took advantage of the global search ability of the differential evolution algorithm and the local search capability of the multi-group gray wolf optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

Wan

2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Duo to strong global search ability, differential evolution (DE) was used for optimizing the parameters of ELM [20], [21], [22]. Based on the elite guidance mechanism and the collaboration mechanism, Li et al proposed a dual mutation strategies collaboration differential evolution [23].…”

Section: Introductionmentioning

confidence: 99%

A Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model of Floating Height in Air Cushion Furnace

et al. 2021

View full text Add to dashboard Cite

In air cushion furnace, the floating height is a key process parameter which greatly affects the quality and production efficiency of high quality mental strips. However, the floating height is hard to be collected in the complex and abominable industry environment. Furthermore, due to the flow field characteristics, some important process variables are difficult to accurately calculate by traditional mechanism modeling methods. In order to accurately predict the floating height, firstly, a low discrepancy heuristic evolution ELM and ground effect theory based serial hybrid soft sensor model is proposed, which constituted by a mechanism model and two data driven models. Secondly, based on the force equilibrium equation and ground effect theory, the mechanism model is constructed, which describes the relationship between the floating height and the process variables including the jet impact angle. Thirdly, a low discrepancy heuristic evolution ELM is proposed as the data driven model to predict the jet impinging angle. In the data driven model, the novel dual mutation strategies collaboration differential evolution is proposed to guarantee the low discrepancy and physical applicability of data driven model. The effectiveness of the proposed method was validated on the self-developed air cushion experiment platform and got desirable experimental results. The research lays an important foundation for the successful implementation of monitoring and control of the strip floating process.

show abstract

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Cited by 10 publications

References 23 publications

An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

A Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model of Floating Height in Air Cushion Furnace

Contact Info

Product

Resources

About