Abstract
Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.
Highlights
Blind Signal Separation (BSS) is a new research field developed in the late 20th century,when there is little available information on the source signal and the transmission channel, and the components of the source signal are extracted or recovered from the observed mixed signal[1]
Lui et al proposed a particle swarm optimization(PSO) based blind separation algorithm in [7], which is not easy to fall into the local extremum, but the parameters are set too much, and the convergence rate is slower; the basic particle swarm optimization was improved in [2], and the convergence speed has been further improved, but it increases the complexity of the algorithm
In order to blindly separate the mixed signal x(t), we propose to use the Whale Optimization Algorithm (WOA) algorithm to solve the separation matrix W in the equation (2)
Summary
Blind Signal Separation (BSS) is a new research field developed in the late 20th century,when there is little available information on the source signal and the transmission channel, and the components of the source signal are extracted or recovered from the observed mixed signal[1]. Blind signal separation is a powerful signal processing method with many potential applications, especially in wireless communications, medical analysis, image processing, speech recognition, blind signal separation has become one of the hot topics in the field of signal processing and artificial neural networks in the world[2] and has important theoretical significance and value. Most methods of blind processing are unsupervised learning methods that construct objective functions based on certain independence criteria. There are stochastic gradient method [3], natural gradient meth-od [4] and FastICA algorithm [5] for the method of calcu-lation of the objective function. As for the gradient algorithm needs to calculate the nonlinear activation function and the pseudo inverse matrix, the calculation is large, the robustness is poor, and the convergence needs to be improved [6], FastICA sensitive to the initial value and other issues. We introduce a method to separate signals by the whale optimization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
