Name
Abstract | ||
|---|---|---|
In this paper, a new control algorithm to combine neural-based learning with error predictor is developed for batch processes. First, the control is represented by a radial basis function (RBF) network within the time horizon. Next, to accommodate the advantages of model predictive control, the error predictor is designed based on the batch iteration direction. Finally, the learning algorithm is derived by guaranteeing the stability. To highlight the key features of the algorithm, an example is provided to demonstrate the performance in a batch process. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ASCC.2015.7244380 | 2015 10th Asian Control Conference (ASCC) |
Keywords | Field | DocType |
Predictive learning control,Radial basis function (RBF) network,Batch process | Convergence (routing),Predictive learning,Radial basis function network,Time horizon,Algorithm design,Radial basis function,Computer science,Model predictive control,Batch processing,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
2072-5639 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| rongmin cao | 1 | 0 | 0.34 |
| Su-Nan Huang | 2 | 505 | 61.65 |
| huixing zhou | 3 | 0 | 0.34 |