Name
Abstract | ||
|---|---|---|
The use of genetic algorithms to design neural networks for real-time control of flows in sewerage networks is discussed. In many control applications, standard supervised learning techniques (such as back-propagation) cannot be used through lack of training data. Reinforcement learning techniques, such as genetic algorithms, are a computationally-expensive but viable alternative if a simulator is available for the system in question. The paper briefly describes why genetic algorithms and neural networks were selected, then reports the results of a feasibility study. This demonstrates that the approach does indeed have merits. The implications of high computational cost are discussed, in terms of scaling up to significantly complex problems. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1007/BF01670149 | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
flow systems,genetic algorithms,neural networks,real-time control,reinforcement learning | Journal | 6.0 |
Issue | ISSN | Citations |
1 | 0941-0643 | 2 |
PageRank | References | Authors |
0.57 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Hunter | 1 | 175 | 11.31 |
| G. Hare | 2 | 2 | 0.57 |
| K. Brown | 3 | 2 | 0.57 |