DCS-ELM: a novel method for extreme learning machine for
By A Mystery Man Writer
Description
Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.
Demystifying Extreme Learning Machines: Part 1 - DEV Community
Extreme Learning Machine - an overview
Demystifying Extreme Learning Machines: Part 1 - DEV Community
A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer
A review on extreme learning machine
PDF) Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction
The condition value comparison of six algorithms on six datasets in 30
PDF) A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines
From concept drift to model degradation: An overview on
PDF] A model updating strategy for predicting time series with
PDF) 1-Norm extreme learning machine for regression and multiclass classification using Newton method
from
per adult (price varies by group size)