关键词: 风压预测, 最小二乘支持向量机, 智能算法, 蚁群优化算法, 粒子群优化算法

Abstract:

Least squares support vector machine (LSSVM)is used to predict wind pressure on building surfaces. To enhance the generalization performance and prediction accuracy of LSSVM for wind pressure, LSSVM based on combination of ant colony optimization (ACO) and particle swarm optimization (PSO) is proposed to find optimal parameters. The combination avoids shortcomings in ACO and PSO, and achieves complementary focus of both. Using LSSVM based on ACO+PSO,wind pressure is forecast. It is compared with ACO-based LSSVM and PSO-based LSSVM, respectively. The numerical analysis shows that the proposed method can improve prediction accuracy and robustness of LSSVM, and has good prospects in engineering applications.

Key words: wind pressure forecast, least square support vector machine (LSSVM), intelligent optimization, ant colony optimization (ACO), particle swarm optimization (PSO)