Modulation signal classification method for cuckoo search-improved gray wolf optimizer-least square support vector machine

Abstract

The invention discloses a modulation signal classification method for cuckoo search-improved gray wolf optimizer-least square support vector machine. The method selects a high-order cumulant and a local mean decomposition amount approximate entropy for the characteristic parameter of a modulation signal, and utilizes cuckoo search for the second update of the wolf position to optimize the two keyparameters of a least squares support vector machine model, namely, the penalty coefficient Gamma and the kernel parameter Sigma, so as to obtain the optimal kernel limit learning machine parameter value. The method reduces the influence of noise factor on the signal recognition result, makes up for the defects of under-envelope, over-envelope and boundary effects in the traditional modal empirical decomposition, and effectively improves the defect that the gray wolf optimization global searching ability is poor and is easy to fall into the local optimal solution in processing of high-dimensional data, compared with the original gray wolf optimization result by MATLAB simulation, is it proved that the method can intelligently classify the modulated signal more efficiently and accurately, and has a good application prospect.

Description

technical field [0001] The invention relates to the field of modulation signal classification and swarm intelligence optimization, in particular to a modulation signal classification method of a cuckoo search improved gray wolf optimization support vector machine. Background technique [0002] The identification of signal modulation means to identify the modulation mode of each signal and its various parameters in the environment of multi-modulation signals and noise interference. In general, the receiver can only intercept the signal whose prior knowledge is unknown, so it becomes more and more important to effectively identify the modulation mode of the signal. [0003] In the published literature related to modulation recognition, the intelligent recognition of signals can be roughly divided into two types: the maximum likelihood hypothesis testing method based on decision theory and the statistical pattern classification and recognition method based on feature extraction...

AI Technical Summary

Problems solved by technology

The defect of GWO's position update method is very obvious: the global search ability is weak, and there is a high probability of falling into a local optimal solution, especially when using high-dimensional data

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