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Incremental learning model based on an improved CKS-PFNN for aluminium electrolysis manufacturing

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Abstract

Filtering neural networks (FNNs) are popular computing frameworks for process system modeling. However, they are vulnerable to non-Gaussian noise and consequently may suffer from low filtering accuracy. To overcome the problem, in this paper, a novel model construction algorithm by combining the improved clustering kernel function smoothing technique and the particle filter neural network (ICKS-PFNN) is proposed. Specifically, ICKS-PFNN firstly presents a construction framework for particle filter neural network (PFNN), which utilizes the dynamic approximation of particles to adjust the NN’s weights and thresholds in real time. Then, the proposed model uses kernel fuzzy C-means algorithm to uncover clusters in the particles of PFNN. A novel proportional distribution sampling strategy is adopted to maintain the diversity in particle clusters, through merging the inferior and superior particles to generate new particles based on the set proportional factors, rather than directly eliminating particles. At last, the estimation of the PFNN model is achieved by utilizing a kernel function smoothing method to update the particles in each cluster. The proposed model has been tested on the real-world system for aluminium electrolysis manufacturing and compared with several closely related frameworks. The experimental results show ICKS-PFNN obtains a superb performance when compared with other baselines. ICKS-PFNN is able to tackle noise and improve the prediction accuracy when dealing with non-Gaussian systems. Successfully applying the proposed framework in aluminium electrolysis manufacturing broadens the practical impact of FNN systems.

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Abbreviations

NN:

Neural network

PF:

Particle filter

EKF:

Extended Kalman filter

UKF:

Unscented Kalman filter

FNN:

Filtering neural network

FCM:

Fuzzy C-mean clustering

MLR:

Multiple linear regression

SSE:

Sum squared error

MAE:

Mean absolute error

RMSE:

Root mean squared error

AEM:

Aluminium electrolysis manufacturing

ICKS:

Improved clustering kernel function smoothing

CKS-PFNN:

Particle filter neural network model based on the clustering kernel function smoothing method

ICS-PFNN:

Particle filter neural network model based on the improved clustering smoothing method

ICKS-PFNN:

Particle filter neural network model based on the improved clustering kernel function smoothing method

EKFNN:

Extended Kalman filter neural network

UKFNN:

Unscented Kalman filter neural network

BPNN:

Back-propagation neural network

PFNN:

Particle filter neural network

PDF:

Probability density function

KFCM:

Fuzzy kernel C-mean clustering

NLMR:

Multiple nonlinear regression

MSE:

Mean squared error

MRE:

Mean relative error

R:

Correlation coefficient

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (No.518 05059), Chongqing Research Program of Basic Research and Frontier Technology under grant (cstc2018jcyjA X0350 and cstc2018jcyjA1663) and Special Project of Technological Innovation and Application Development in Chongqing (No. cstc2019jscx-msxmX0054).

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Authors and Affiliations

  1. School of Mechanical Engineering, Sichuan University, Chengdu, 610065, People’s Republic of China

    Wei Ding, Lizhong Yao, Yanyan Li & Wei Long

  2. School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing, 401331, People’s Republic of China

    Lizhong Yao & Jun Yi

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  1. Wei Ding
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Correspondence to Lizhong Yao.

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Ding, W., Yao, L., Li, Y. et al. Incremental learning model based on an improved CKS-PFNN for aluminium electrolysis manufacturing. Neural Comput & Applic 34, 2083–2102 (2022). https://doi.org/10.1007/s00521-021-06530-5

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