Adaptive Filtering for Bioreactor Temperature Signals: A Novel Neural Network and Kalman Filter Approach

This paper proposes an improved adaptive filtering algorithm based on neural network and Kalman filter for bioreactor temperature signal processing. The algorithm combines neural network and Kalman filter, using neural network to predict and correct the original signal, and using Kalman filter to filter and smooth the corrected signal. Experimental results show that compared with traditional filtering algorithms, this algorithm has higher filtering accuracy and better anti-interference ability, which can effectively improve the accuracy and stability of bioreactor temperature signal processing.