Signal Pattern Recognition Based on Fractal Features and Machine Learning
Abstract
:1. Introduction
2. Related Work
3. Fractal Dimension
3.1. Fractal Box Dimension
3.2. Katz Fractal Dimension
3.3. Higuchi Fractal Dimension
3.4. Petrosian Fractal Dimension
3.5. Sevcik Fractal Dimension
4. Classifier Algorithm
4.1. BP Neural Network
Algorithm: BP Neural Network Classifier |
Input: training dataset, testing dataset Output: classification result (label out) 1. Network initialization 2. Calculate the output of the network 3. Adjust the neurons’ weight in the network 4. Achieve the minimum value of the objective function 5. Output the classification result |
4.2. Grey Relational Analysis
Algorithm: Grey Relational Analysis |
Input: training dataset, testing dataset Output: classification result (label_out) 1. Determining the comparative sequence 2. For i = 1 to K Calculate the correlation degree End 3. = max() 4. Label out = find () 5. End |
4.3. K-Nearest Neighbor
Algorithm: KNN Classifier |
Input: training dataset, testing dataset Output: classification result (label out) 1. Calculate the weight of characteristic: 2. Calculate the vector space model of the training sample and the sample to be tested: 3. The distance of samples: 4. For i = 1 to K Calculate the weight of characteristic item: 5. = max() 6. Label out = find () 7. End |
4.4. Random Forest
Algorithm: Random Forest Classifier |
Input: training dataset, testing dataset Output: classification result (label out) 1. Training J random forest classifiers 2. For i = 1 to J Calculate each probability of every classifier : End 3. Use Dempster–Shaferevidence theory to calculate each result probability of every classifier 4. = max() 5. Label out = find () 6. End |
5. Simulation Results
5.1. Simulation Analysis of Fractal Features
5.2. Classification Results
6. Conclusions
Funding
Conflicts of Interest
References
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Type | Box | Katz | Higuchi | Petrosian | Sevcik |
---|---|---|---|---|---|
SVar/ | 2.5 | 1.88 | 9 | 0.03 | 9.3 |
Type | Box | Katz | Higuchi | Petrosian | Sevcik |
---|---|---|---|---|---|
Runtime/s | 1.92 | 1.66 | 82.199 | 2.83 | 1.26 |
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Shi, C.-T. Signal Pattern Recognition Based on Fractal Features and Machine Learning. Appl. Sci. 2018, 8, 1327. https://0-doi-org.brum.beds.ac.uk/10.3390/app8081327
Shi C-T. Signal Pattern Recognition Based on Fractal Features and Machine Learning. Applied Sciences. 2018; 8(8):1327. https://0-doi-org.brum.beds.ac.uk/10.3390/app8081327
Chicago/Turabian StyleShi, Chang-Ting. 2018. "Signal Pattern Recognition Based on Fractal Features and Machine Learning" Applied Sciences 8, no. 8: 1327. https://0-doi-org.brum.beds.ac.uk/10.3390/app8081327