Temperature compensation of pH sensor based on improved adaptive genetic neural network

Xin Zhang; Zhangjun Peng; Li Li; Shuai Zhou; Zhigui Liu

doi:10.1117/12.3013279

8 December 2023 Temperature compensation of pH sensor based on improved adaptive genetic neural network

Xin Zhang, Zhangjun Peng, Li Li, Shuai Zhou, Zhigui Liu

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 1294305 (2023) https://doi.org/10.1117/12.3013279
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

Aiming at the problem that pH sensor based on ion sensitive field effect transistor (ISFET) will produce temperature drift when temperature changes, a compensation method based on the combination of improved adaptive genetic algorithm (IAGA) and BP neural network is studied. After the sensor compensation network model is established, this method first uses an improved algorithm to improve the weight threshold of the BP neural network, and then trains the BP neural network constructed using the optimized weight threshold. Finally, the model obtained after training is implemented to sensor compensate. Experimental results show that the compensation error of the IAGA-BP model is less than 3%, and compared with the BP, RBF and AGA-BP models, the IAGA-BP model has better stability.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Zhang, Zhangjun Peng, Li Li, Shuai Zhou, and Zhigui Liu "Temperature compensation of pH sensor based on improved adaptive genetic neural network", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 1294305 (8 December 2023); https://doi.org/10.1117/12.3013279

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