Name
Abstract | ||
|---|---|---|
Accurate forecast of urban water consumption is the basis of urban water supply network planning and design, and provides a scientific basis for water production and scheduling. Because the convergence speed of RBF neural network and accuracy of urban water consumption forecast based on RBF neural network are too low, we proposed a new forecast method based on QPSO-RBF neural network. In this method, the parameters of RBF neural network are optimized by QPSO, and then used the QPSO-RBF neural network to forecast urban water daily consumption. The experimental results show that both convergence speed and accuracy of the proposed method are better than the method based on RBP and PSO-RBF neural network. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CIS.2012.59 | CIS |
Keywords | Field | DocType |
rbf neural network,design,radial basis function networks,scheduling,urban water consumption,forecast,forecasting theory,quantum particle swarm optimization,particle swarm optimisation,convergence speed,urban water consumption forecast,accurate forecast,urban water supply network design,pso-rbf neural network,urban water supply network,water scheduling,urban water supply network planning,new forecast method,qpso-rbf neural network,water production,water supply,urban water consumption forecasting,urban water | Convergence (routing),Mathematical optimization,Computer science,Water supply network,Scheduling (computing),Water consumption,Quantum particle swarm optimization,Artificial intelligence,Artificial neural network,Forecasting theory,Machine learning,Water supply | Conference |
ISBN | Citations | PageRank |
978-1-4673-4725-9 | 2 | 0.48 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingtong Zhu | 1 | 2 | 0.48 |
| Bo Xu | 2 | 30 | 6.47 |