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Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model
| Content Provider | Semantic Scholar |
|---|---|
| Author | Guangming, Zeng Xiao-Dong, Li Guo-He, Huang Jian-Bing, Li Ru, Jiang |
| Copyright Year | 2007 |
| Abstract | By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed, with the assumption that the series was predictable. Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction. Reasonable forecasting results were achieved using this method. |
| Starting Page | 334 |
| Ending Page | 338 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/s11783-007-0057-6 |
| Volume Number | 1 |
| Alternate Webpage(s) | https://page-one.springer.com/pdf/preview/10.1007/s11783-007-0057-6 |
| Alternate Webpage(s) | https://doi.org/10.1007/s11783-007-0057-6 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |