针对SVM(Support Vector Machine,支持向量机)存在支持向量个数较多、核函数要求严格等不足,将性能更出色的RVM((Relevance Vector Machine,相关向量机)用于大坝安全预警模型的构建。核函数及其参数对RVM模型的性能有着重要的影响,组合局部核函数和全局核函数的混和核函数能提高模型的拟合精度和泛化能力,利用PSO(Particle Swarm Optimization,粒子群算法)能有效地对核参数进行寻优,针对标准PSO算法容易陷入局部最优点的缺陷,提出IPSO(Improved Particle Swarm Optimization,改进的粒子群算法)。将上述组合算法用于大坝安全模型的建立,实例分析表明,基于上述算法模型的性能得到了一定程度的提高。
Abstract
In view of the disadvantages of SVM (support vector machine) such as a large number of support vectors and strict demand for kernel function, we introduce RVM (relevance vector machine) to establish dam safety model which has better performance. Kernel function and its parameters have important effects on the performance of the RVM model. Mixed kernel function in association with local and global kernels can improve the fitting accuracy and generalization ability of the model. The optimized parameters of the kernel function can be effectively found by using PSO (particle swarm optimization) algorithm. However, the defect of local optimal point easily occurs in normal PSO algorithm. In light of this, we apply an algorithm of improved particle swarm optimization (IPSO). On the basis of combined algorithms above, we establish a model for dam safety, and the results indicate that the performance of RVM model with hybrid kernel is superior to that of conventional model.
关键词
大坝安全建模 /
相关向量机 /
混合核函数 /
自适应粒子群算法 /
拟合精度 /
泛化能力
Key words
dam safety modeling /
relevance vector machine /
hybrid kernel function /
adaptive particle swarm optimization /
fitting accuracy /
generalization ability
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参考文献
[1] 苏怀智, 吴中如, 戴会超. 初探大坝安全智能融合监控体系[J]. 水力发电学报, 2005, 24(1): 122-126.
[2] SU Huai-zhi, HU Jiang, WU Zhong-ru. A Study of Safety Evaluation and Early-warning Method for Dam Global Behavior[J]. Structural Health Monitoring, 2012, 11(3): 269-279.
[3] 张孟尧.基于相关向量机的生物反应过程软测量建模与应用[D]. 镇江:江苏大学,2013.
[4] 纪雪玲,李 明,李 玮,等.一种克服局部最优的收缩因子PSO算法[J].计算机工程,2011,37(20):213-215.
[5] 杨树仁,沈洪远.基于相关向量机的机器学习算法研究与应用[J].计算技术与自动化,2010,29(1):43-47.
[6] TIPPING M E. Sparse Bayesian Learning and the Relevance Vector Machine[J]. The Journal of Machine Learning Research, 2001, 1: 211-244.
[7] BISHOP C M. Pattern Recognition and Machine Learning[M]. New York: Springer, 2006.
[8] TIPPING M E, FAUL A. Fast Marginal Likelihood Maximization for Sparse Bayesian Models[C]//Society for Artificial Intelligence and Statistics. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, Florida, January 3-6,2003:1-8.
[9] 唐 奇,王红瑞,许新宜,等.基于混合核函数SVM水文时序模型及其应用[J].系统工程理论与实践, 2014, 34(2): 521-529.
[10]郑志成,徐卫亚,徐 飞,等.基于混合核函数PSO-LSSVM的边坡变形预测[J].岩土力学,2012,33(5):1421-1426.
[11]刘华蓥,林玉娥,齐名军,等.求解约束优化问题的改进粒子群算法[J].大庆石油学院学报,2005,29(4):73-75.
[12]ASANGA R,SAMAN K H,HARRY C W.Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients[J].IEEE Transactions on Evolutionary Computation,2004,8(3):240-255.
[13]OH H S, KIM D, LEE Y, et al.Cross-validated Wavelet Shrinkage [J].Computational Statistics, 2009,24(3):497-512.
基金
国家自然科学基金项目(51179066);高等学校博士学科点专项科研基金资助课题项目(20130094110010);江苏省杰出青年基金项目(BK2012036);水利部公益性行业科研专项经费项目(201301061)
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