文献类型：期刊文献

英文题名：A Two-Stage Multi-Target Domain Adaptation Framework for Prediction of Key Performance Indicators Based on Adversarial Network

作者：Su, Cheng[1];Peng, Xin[1];Yang, Dan[1];Li, Zhi[1];Wu, Xiaolong[2,3];Zhong, Weimin[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing Key Lab Computat Intelligence & Intelligen, Engn Res Ctr Digital Community,Minist Educ, Beijing 100021, Peoples R China;[3]Beijing Univ Technol, Beijing Lab Intelligent Environm Protect, Beijing 100021, Peoples R China

年份：2024

卷号：8

期号：2

起止页码：1772

外文期刊名：IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE

收录：;EI(收录号：20240715545195);WOS:【SCI-EXPANDED(收录号:WOS:001164236500001)】；

基金：No Statement Available

语种：英文

外文关键词：Task analysis; Feature extraction; Generators; Adaptation models; Training; Generative adversarial networks; Data models; Domain adaptation; autoencoder; generative adversarial network; wastewater treatment process; effluent quality index

摘要：In wastewater treatment processes, building performance evaluation models to predict key indicators under uncommon operating conditions is difficult due to the lack of labeled data. Domain adaptation can be used to solve this problem through leveraging the knowledge of common conditions to construct prediction models for uncommon conditions. Considering the costs of labeling data, it is reasonable to assume that only data from the most common condition are labeled. Therefore, all domain adaptation tasks share a source domain. Under this assumption, most domain adaptation methods require mapping the same source data multiple times and training multiple task-specific predictors in different tasks, resulting in additional computational costs. To give a solution, a stepwise domain alignment strategy is proposed, which consists of two steps. First, the latent features of source domain are extracted, and the features are fixed after this step. Second, target domains from different tasks are mapped to the fixed feature space to achieve domain alignment. Based on the strategy, a two-stage multi-target adversarial adaptation network (TS-MAAN) for predicting effluent quality index is proposed, which consists of an autoencoder and a generative adversarial network. Additionally, parameter initialization and multi-kernel maximum mean discrepancy optimization are further proposed to improve the stability and prediction accuracy of the TS-MAAN, respectively. Experiments conducted on datasets generated by the Benchmark Simulation Model No.1 demonstrate that TS-MAAN exhibits excellent prediction accuracy and stability, while enabling efficient multi-target domain adaptation. Moreover, these experiments verify the effectiveness of parameter initialization and MK-MMD optimization.