Purpose

This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations.

Design/methodology/approach

The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept.

Findings

Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique.

Originality/value

Authors confirm that this manuscript is original.

中文翻译：

---

使用神经增强学习机技术实时识别混沌植物

目的

本文旨在提出一种新的基于神经的增强型极限学习机 (EELM) 算法，用作在线自适应估计模型，针对未确定的系统动力学并包含内部/外部扰动。

设计/方法/方法

EELM 结构基于单层前馈神经 (SLFN) 模型，其中隐藏的加权系数随机启动，SLFN 的加权输出使用 Lyapunov 稳定性概念实现的在线自适应规则进行在线修改。

发现

四个不同的基准不确定混沌系统测试已经得到令人满意的研究，以证明所提出的 EELM 技术的优越性。

原创性/价值

作者确认这份手稿是原创的。