Abstract
Aiming at the fault diagnosis characteristics of LED landscape lighting equipment, a class of genetic algorithm improved particle swarm optimization optimized wavelet neural network model is constructed. This fusion algorithm introduces the idea of cross factors and inertia weights in the genetic algorithm to the basic particle swarm optimization algorithm, and adjusts for the traits of the standard wavelet neural network that has a slow convergence rate and might fall into local extreme values. The simulation results prove that this fusion algorithm can be efficaciously applied to the fault diagnosis of LED landscape lighting equipment and meet the needs of real-time monitoring of equipment.
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