Identification of fast-changing signals by means of adaptive chaotic transformations

TL;DR

This paper introduces an adaptive chaotic transformation method for real-time identification of fast-changing signals, applicable in fields like chemical control and medicine, demonstrated on the Weierstrass function and ECG signals.

Contribution

It presents a novel adaptive sampling technique based on chaotic mapping for identifying rapidly changing signals in real-time.

Findings

Effective identification of Weierstrass function signals

Successful real-time ECG signal analysis

Potential for online control applications

Abstract

The adaptive approach of strongly non-linear fast-changing signals identification is discussed. The approach is devised by adaptive sampling based on chaotic mapping in yourself of a signal. Presented sampling way may be utilized online in the automatic control of chemical reactor (throughout identification of concentrations and temperature oscillations in real-time), in medicine (throughout identification of ECG and EEG signals in real-time), etc. In this paper, we presented it to identify the Weierstrass function and ECG signal.