Temperature compensation for humidity sensors using ISSA-BP neural network

High-precision humidity detection is essential in various fields. However, the sensor's output signal is often affected by complex environmental temperature changes. To address these challenges, this paper propose an improved sparrow search algorithm based on the back propagation neural network (ISSA-BP). This method optimizes the initialization process of the traditional algorithm and introduces an edge evolution strategy to enhance its iterative update process, significantly improving both efficiency and accuracy. Experimental results demonstrate that the proposed algorithm reduces the mean absolute percentage error (MAPE) from 4.35% to 0.92% and decreases the convergence time from 1.25 s to 0.65 s, compared to traditional methods.