METHOD OF CLUSTERIZATION OF QUASIPERIODIC SIGNAL BASED ON CLONAL SELECTION ALGORITHM
DOI:
https://doi.org/10.24025/2306-4412.2.2022.253905Keywords:
quasi-periodic signal, clonal selection algorithm, hierarchical-iterative clustering, signal transformation, signal structure analysisAbstract
The paper proposes a hierarchical-iterative method of clustering of a quasi-periodic signal based on clonal selection algorithm, which increases the speed and accuracy of clustering. Preliminary transformation of samples (quasi-periodic areas) of this signal into a single amplitude-time window based on shift and scaling in time and amplitude, linear interpolation and time sampling is performed. Signals processed by intelligent computer systems for identification, analysis and synthesis, storage of digital signals (such as acoustic, graphic, vibration, electrogram, communication ones, etc.) are often quasi-periodic. This raises the problem of constructing effective methods for analyzing the structure of a quasi-periodic signal. One of the means of analyzing the structure of a quasi-periodic signal is clustering, which is a type of machine learning without a teacher. As a result of the analysis of modern methods of clustering of quasi-periodic signals, it has been found that most of them have one or more of the following disadvantages: unknown exact number of clusters; sensitivity to initial values of the centroids of clusters; low probability of clustering; low speed of clustering; comparison of signal areas that have only the same size; comparison of only binary signals. Therefore, it is important to develop a method for transforming a quasi-periodic signal and a hierarchical-iterative method of clustering based on a clonal selection algorithm. This will increase the efficiency for the analysis of quasi-periodic signal structure in digital data processing in intelligent computer systems of identity identification, technical and medical diagnostics, network traffic analysis, etc. A comparison of the method proposed by the authors and existing clustering methods is given, with the cloning parameter α = 0.1, the mutation parameter β = 2.5, the number of replacement antibodies d = 0.2 | H |. Clustering has been performed on quasi-periodic speech sounds uttered by different speakers. A method for converting a quasi-periodic signal, which converts samples (quasi-periodic areas) of this signal into a single amplitude-time window by shifting and scaling in time and amplitude, interpolation and sampling, is proposed. This allows to compare signal samples of different lengths and with different amplitudes. A method for clustering a quasi-periodic signal based on a hierarchical-iterative approach and a clonal selection algorithm has been developed, which reduces the sensitivity to initial values of cluster centroids due to random search and provides adaptive adjustment of the cluster number due to the hierarchical approach, and also increases the probability of clustering to 0.98.
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Copyright (c) 2022 Федоров Євген Євгенович, Уткіна Тетяна Юріївна
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