Next Article in Journal
Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
Next Article in Special Issue
GARLM: Greedy Autocorrelation Retrieval Levenberg–Marquardt Algorithm for Improving Sparse Phase Retrieval
Previous Article in Journal
Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest
Previous Article in Special Issue
Fractal Behavior of Particle Size Distribution in the Rare Earth Tailings Crushing Process under High Stress Condition
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(8), 1327;

Signal Pattern Recognition Based on Fractal Features and Machine Learning

College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Received: 5 July 2018 / Revised: 22 July 2018 / Accepted: 23 July 2018 / Published: 8 August 2018
(This article belongs to the Special Issue Fractal Based Information Processing and Recognition)
Full-Text   |   PDF [2440 KB, uploaded 9 August 2018]   |  


As a typical pattern recognition method, communication signal modulation involves many complicated factors. Fractal theory can be used for signal modulation feature extraction and recognition because of its good ability to express complex information. In this paper, we conduct a systematic research study by using the fractal dimension as the feature of modulation signals. Box fractal dimension, Katz fractal dimension, Higuchi fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension are extracted from eight different modulation signals for signal pattern recognition. Meanwhile, the anti-noise function, box-diagram, and running time are used to evaluate the noise robustness, separability, and computational complexity of five different fractal features. Finally, Bback-Propagation (BP) neural network, grey relation analysis, random forest, and K-nearest neighbor are proposed to classify the different modulation signals based on these fractal features. The confusion matrices and recognition results are provided in the experimental section. They indicate that random forest had a better recognition performance, which could reach 96% in 10 dB. View Full-Text
Keywords: pattern recognition; fractal dimension; feature evaluation; random forest classifier pattern recognition; fractal dimension; feature evaluation; random forest classifier

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Shi, C.-T. Signal Pattern Recognition Based on Fractal Features and Machine Learning. Appl. Sci. 2018, 8, 1327.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top