Automatic modulation recognition using time domain parameters
Signal Processing
Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
On finding the number of clusters
Pattern Recognition Letters
Automatic Modulation Recognition of Communication Signals
Automatic Modulation Recognition of Communication Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent control of signal processing algorithms in communications
IEEE Journal on Selected Areas in Communications
Online modulation recognition of analog communication signals using neural network
Expert Systems with Applications: An International Journal
A novel hybrid algorithm for function approximation
Expert Systems with Applications: An International Journal
Comparison of envelope extraction algorithms for cardiac sound signal segmentation
Expert Systems with Applications: An International Journal
A novel method for measuring semantic similarity for XML schema matching
Expert Systems with Applications: An International Journal
A transaction pattern analysis system based on neural network
Expert Systems with Applications: An International Journal
Multiclass least-squares support vector machines for analog modulation classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Clustering of machining signal for verifying machining parameter
SENSIG'08 Proceedings of the 1st WSEAS international conference on Sensors and signals
Hi-index | 12.06 |
This study introduces a comparative study of implementation of clustering algorithms on classification of the analog modulated communication signals. A number of key features are used for characterizing the analog modulation types. Four different clustering algorithms are used for classifying the analog signals. These most representative clustering techniques are K-means clustering, fuzzy C-means clustering, mountain clustering and subtractive clustering. Performance comparison of these clustering algorithms and the advantages and disadvantages of the methods are examined. The validity analysis is performed. The study is supported with computer simulations.