DCS-ELM: a novel method for extreme learning machine for

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.

PeerJ - Just published in PeerJ Computer Science - DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC Read the full article #

Introduction to Extreme Learning Machines, by Kemal Erdem (burnpiro)

Concept of Extreme learning machines (ELM)

Evolution of Extreme Learning Machines - Blog by Kemal Erdem

Extreme Learning Machine - an overview

PeerJ - Profile - Mustafa Ulas

DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC [PeerJ]

Symmetry, Free Full-Text

Extreme learning machines: a survey International Journal of Machine Learning and Cybernetics

Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction - ScienceDirect

Frontiers Functional extreme learning machine

A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer

A new non-monotonic infeasible simplex-type algorithm for Linear Programming [PeerJ]

Water, Free Full-Text

Symmetry, Free Full-Text