Name
Abstract | ||
|---|---|---|
To solve the strong nonlinearity and data deterioration due to missing, outliers contained in the training data, this paper combines robust EMPCA (Expectation Maximization Principle Component Analysis) and the error-based input weights updating NNPLS (Neural Network Partial Least Square) to build a nonlinear and robust model as a software sensor of effluent quality indices for the anoxic-aeration activated sludge with nitrogen removal process in wastewater treatment pants. As the first step, data preprocessing based on the modified robust EMPCA is used to eliminate gross error, estimate missing data. Then an error-based input weights updating NNPLS (EB-NNPLS) is further used to predict effluent quality indices. This study compares the performance of partial least squares (PLS) regression analysis, polynomial PLS, NNPLS and EB-NNPLS with robust nonlinearity for the prediction of effluent quality. Simulations results for industrial process data show that the proposed method outperforms basic PLS, the polynomial PLS and NNPLS for the prediction of effluent quality indices. ©2011 ACADEMY PUBLISHER. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.4304/jsw.6.6.1067-1074 | JSW |
Keywords | Field | DocType |
expectation maximization (em),neural netowrk partial least square (nnpls),partial least square (pls),principal component analysis | Least squares,Computer science,Expectation–maximization algorithm,Regression analysis,Partial least squares regression,Data pre-processing,Outlier,Artificial intelligence,Missing data,Machine learning,Principal component analysis | Journal |
Volume | Issue | Citations |
6 | 6 | 1 |
PageRank | References | Authors |
0.38 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lijie Zhao | 1 | 41 | 9.72 |
| De-cheng Yuan | 2 | 12 | 2.85 |
| Jian Tang | 3 | 526 | 148.30 |
| Wei Wang | 4 | 1679 | 168.84 |
| Tianyou Chai | 5 | 2014 | 175.55 |