直升机旋翼自转着陆过程的支持向量机模型  

Support Vector Machine Model for Landing Process of Helicopter with Rotator Self-rotating

作　　者：王书舟[1] 伞冶[1] 王书文[2] 

机构地区：[1]哈尔滨工业大学,哈尔滨150001 [2]苏州科技学院土木工程学院,苏州215011

出　　处：《系统仿真学报》2009年第3期707-710,共4页Journal of System Simulation

基　　金：国家自然科学基金(60474069)

摘　　要：把支持向量机方法引入到直升机智能化建模领域,用支持向量机建立了直升机自转着陆过程的旋翼转速模型,并采用序列最小优化算法求解。与神经网络模型相比,直升机的支持向量机模型具有结构简单、运算速度快、泛化能力高等特点。理论分析和仿真结果表明,应用支持向量机建立直升机的仿真模型是切实可行的。Support Vector Machine （SVM） method was introduced to the field of intelligent modeling for helicopter, and the rotator speed model for landing process of helicopter with rotator self-rotating was built. Sequential Minimal Optimization （SMO） was adopted to numerically solve this optimization problem. Compared with the Neural Network model, the SVM simulation model of helicopter owns some advantages such as simple structure, fast convergence speed and high generalization ability. It is shown by theoretic analysis and simulation result that the SVM method is feasible.

关 键 词：支持向量机 直升机 仿真模型 泛化能力 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]