In this paper, neural network is applied to adaptive noise cancellation technology, and an improved BP algorithm for adaptive noise cancellation is proposed. Firstly, the principle of adaptive noise cancellation technology is introduced briefly, and then some commonly used adaptive algorithms in noise cancellation technology are summarized. Then the adaptive noise cancellation system based on neural network is simulated in MATLAB from two aspects. When two noise signals are linearly correlated, two different signals are used to simulate the system. The advantages and disadvantages of each algorithm are verified by comparing the simulation results. Two different input signals are used to analyze the ability of removing noise when two noise signals are non-linearly correlated, and the adaptive noise cancellation system based on LMS is compared. Finally, the performance of the system is analyzed from three aspects: denoising ability, convergence speed and mean square error. Finally, the measured data are used in the adaptive noise cancellation system. The simulation results verify its effectiveness and the performance of the adaptive noise cancellation system based on neural network is better.
Neural network Adaptive system Noise cancellation MATLAB simulation
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1. The study was supported by “National level -National key project of research and development plan (2016YFC1402500)”.
2. The study was supported by “The dynamic characteristics of PM2.5 dust based on multidimensional multiphase particle swarm turbulence model” (the national natural science foundation project:51704147).
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