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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Guo Wenxian Wang Hongxiang Xu Jianxin Zhang Yunfeng |
| Copyright Year | 2010 |
| Abstract | Based on the observed river runoff data obtained from Yichang hydrological station in the middle of the Yangtze River, Radial Basis Function neural network (RBF) based on improved particle swarm optimization (PSO) was applied to predict river runoff in the Yangtze River. The capacity of solving nonlinear problems is enhanced effectively through adjusting inertia factor dynamically in the algorithm of particle swarm optimization. Improved PSO is applied to optimize the parameters of the neural network and overcome the over-fitting problem and a faster convergence rate is reached. MATLAB was applied to simulate the model. The theoretical analysis and simulations show that the prediction model is more practical and has better generalization performance and prediction accuracy than the traditional one. |
| Starting Page | 968 |
| Ending Page | 971 |
| File Size | 272348 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424472796 |
| e-ISBN | 9781424472802 |
| DOI | 10.1109/ICICTA.2010.504 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-11 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | RBF neural network prediction model Heuristic algorithms river runoff Predictive models Rivers Particle swarm optimization MATLAB Convergence Neural networks improved Particle Swarm Optimization Radial basis function networks Performance analysis Mathematical model |
| Content Type | Text |
| Resource Type | Article |
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