Remote Sensing Inversion of Saline and Alkaline Land Based on an Improved Seagull Optimization Algorithm and the Two-Hidden-Layer Extreme Learning Machine

2021 ◽  
Author(s):  
Dong Xiao ◽  
Lushan Wan
Keyword(s):  
Remote Sensing ◽  
Extreme Learning Machine ◽  
Optimization Algorithm ◽  
Learning Machine ◽  
Hidden Layer

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

2021 ◽  
Author(s):  
Di Wu ◽  
Ting Li ◽  
Qin Wan
Keyword(s):  
Extreme Learning Machine ◽  
Optimization Algorithm ◽  
Optimization Methods ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Intelligent Optimization ◽  
Intelligent Optimization Algorithm ◽  
Mutation Strategies ◽  
Learning Machine ◽  
Hidden Layer

AbstractThe 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.

Color difference classification of solid color printing and dyeing products based on optimization of the extreme learning machine of the improved whale optimization algorithm

2019 ◽  
Vol 90 (2) ◽  
pp. 135-155 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Jianxin Zhang ◽  
Zefei Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Color Difference ◽  
Classification Model ◽  
Initial Population ◽  
Whale Optimization ◽  
Color Printing ◽  
Learning Machine ◽  
Hidden Layer

To mitigate the problem of low classification accuracy in solid color printing and dyeing, a color difference classification model based on the differential evolution (DE) improved whale optimization algorithm (WOA) for extreme learning machine (ELM) optimization, named the DE–WOA–ELM, was developed in this study. Considering that the initial population of the WOA has a significant influence on the solution speed and quality, DE was used to generate a more suitable initial population for the WOA by avoiding local optima, thereby improving the performance. The method used an excellent global search ability to improve the WOA for optimization and obtained an optimal parameter combination for the ELM. Thus, the problem of randomly initializing the input weight and the hidden layer bias of the ELM, which leads to a nonuniform training model and unstable algorithm, was solved. Finally, by optimizing the input weight and hidden layer bias, the color difference classification model of the ELM with a strong generalization ability was constructed. The results of the color difference classification experiments on fabric images collected under standard light sources show that the average classification accuracy for the dataset is increased by 2.15%, 11.06%, 12.11%, and 0.47% compared with those of the ELM, support vector machine, back propagation neural network, and kernel ELM, respectively.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Negative Correlation Hidden Layer for the Extreme Learning Machine

2021 ◽  
pp. 107482
Author(s):  
Carlos Perales-González ◽  
Francisco Fernández-Navarro ◽  
Javier Pérez-Rodríguez ◽  
Mariano Carbonero-Ruz
Keyword(s):  
Extreme Learning Machine ◽  
Negative Correlation ◽  
Learning Machine ◽  
Hidden Layer

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise

2014 ◽  
Vol 989-994 ◽  
pp. 3679-3682 ◽  
Author(s):  
Meng Meng Ma ◽  
Bo He
Keyword(s):  
Extreme Learning Machine ◽  
Simple Structure ◽  
Learning Algorithm ◽  
Deep Structure ◽  
Noisy Data ◽  
Machine Learning Algorithm ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Auto Associative Neural Network

Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

Dynamic adjustment of hidden layer structure for convex incremental extreme learning machine

2017 ◽  
Vol 261 ◽  
pp. 83-93 ◽  
Author(s):  
Yongjiao Sun ◽  
Yuangen Chen ◽  
Ye Yuan ◽  
Guoren Wang
Keyword(s):  
Extreme Learning Machine ◽  
Layer Structure ◽  
Dynamic Adjustment ◽  
Learning Machine ◽  
Hidden Layer
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