Abstract
To reduce the influence of temperature on difluoromethane (R32) gas sensors using non-dispersive infrared (NDIR), a temperature compensation method based on improved whale optimization algorithm (IWOA) and back propagation neural network (BPNN) is proposed. The IWOA that uses tent chaotic mapping to optimize the initial population of the whale optimization algorithm (WOA) and incorporates adaptive weights in the population iterations is used to optimize the weights and thresholds between nodes in each layer of the BPNN. The optimized BPNN is used to train the temperature compensation model. The experimental results show that the back propagation neural network optimized with improved whale optimization algorithm (IWOA-BPNN) reduces the maximum relative error to 1.33%, compared to 12.46 and 11.66% for the traditional BPNN and the back propagation neural network optimized with whale optimization algorithm (WOA-BPNN), respectively. IWOA-BPNN is also compared with previously reported algorithms, and the results show that the algorithm improves the accuracy of the sensor.
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