Design of intelligent lighting control based on multidata source ANFIS-BPNN model

The rapid advancement of computer technology has driven lighting systems toward intelligent and modernized development. To achieve precise smart lighting control, this study employs multiple sensors for environmental monitoring, utilizes wavelet transform for signal processing, and implements adaptive control through an adaptive neural fuzzy inference system combined with backpropagation neural networks, thereby establishing a lighting control model. Performance verification experiments demonstrated that the proposed model exhibits a maximum relative error of 1.13lx between its illumination adjustment curves and habitual lighting curves, while maintaining energy consumption at 50.50W in daily lighting scenarios—both metrics surpassing comparison models. The experimental results confirm that the proposed model outperforms existing counterparts in both control accuracy and energy efficiency, effectively fulfilling lighting control tasks and providing a scientifically grounded theoretical foundation for intelligent lighting control technology development.