A novel improved adaptive filtering algorithm based on neural network and kalman filter for bioreactor temperature signal processing摘要

In this paper, we propose a novel improved adaptive filtering algorithm based on neural network and Kalman filter for bioreactor temperature signal processing. The algorithm combines the advantages of both neural network and Kalman filter to improve the accuracy and stability of the temperature signal processing.

The neural network is used to model the bioreactor temperature signal, and the Kalman filter is used to estimate the state of the system and to filter out the noise in the signal. The neural network is trained using the backpropagation algorithm, and the Kalman filter is optimized using the maximum likelihood estimation method.

Experimental results show that the proposed algorithm can effectively filter out the noise in the bioreactor temperature signal, and improve the accuracy and stability of the temperature signal processing. Compared with other traditional filtering algorithms, the proposed algorithm has better performance in terms of accuracy and stability.

The proposed algorithm has potential applications in the bioreactor temperature signal processing, and can provide a reliable and accurate method for monitoring and controlling the bioreactor temperature