3 years ago

# An improved feed-forward neural network based on UKF and strong tracking filtering to establish energy consumption model for aluminum electrolysis process

Jun Yi, Wei Long, Lizhong Yao, Taifu Li, Yanyan Li

### Abstract

The paper presents a modeling method about the energy consumption of aluminum electrolysis process based on a new neural network. The proposed neural network (NN) is built by combining two theories of unscented Kalman filtering (UKF) and strong tracking filtering (STF), which is shortened as STUKFNN in this study. Moreover, the new training algorithm and robustness analysis of the STUKFNN are presented. The final section of the paper shows an illustrative example regarding the application of the new training algorithm to estimate the technical energy consumption of the aluminum electrolysis process, compared with the modeling methods of back-propagation neural network (BPNN), extended Kalman filtering neural network (EKFNN) and unscented Kalman filtering neural network (UKFNN). The analysis and results show that the method improves the real-time tracking ability of dynamic interference in aluminum electrolysis process, and the accuracy of STUKFNN is better than the other three modeling methods. The average indicators MAE, MSE, R of the STUKFNN based on 30 runs are 15.4793, 1862.65 and 0.9966, respectively, which are all superior to other methods. The proposed method also shows better performance compared with UKFNN, EKFNN and BPNN by the proportion of relative error (RE) in the interval $$|{\mathrm{RE}}|<0.1\%$$ based on all samples, 76, 38, 22 and 74%, respectively.

DOI: 10.1007/s00521-018-3357-9

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.