Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
406382 | Neurocomputing | 2015 | 11 Pages |
Abstract
This paper proposes an improved dynamic particle swarm optimization algorithm, which uses a new and effective exponential decreasing inertia weight (EDIW) strategy. Based on the improved EDIW-PSO algorithm together with AdaBoost algorithm, we adjust the parameters (centers, widths, shape parameters and connection weights) of GRBF and present a novel hybrid EDIW-PSO-AdaBoost-GRBF model. Two application examples are given on the proposed model. The results obtained show that the proposed model is effective and feasible for prediction problems.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jinna Lu, Hongping Hu, Yanping Bai,