机械设计与研究
機械設計與研究
궤계설계여연구
MACHINE DESIGN AND RESEARCH
2010年
1期
65-68
,共4页
小波神经网络%非正态随机参数%多失效模式%可靠性优化设计%随机摄动技术%Edgewoah级数
小波神經網絡%非正態隨機參數%多失效模式%可靠性優化設計%隨機攝動技術%Edgewoah級數
소파신경망락%비정태수궤삼수%다실효모식%가고성우화설계%수궤섭동기술%Edgewoah급수
wavelet neural network%non-normal random parameter%multi-failure modes%reliability design optimization%probabilistic perturbation technology%Edgeworth series
针对多失效模式的结构系统的可靠性优化问题,提出了随机模拟-小波神经网络方法(MCS-WNN),将可靠性优化设计中的非正态随机参数的概率约束转化为等价的确定性约束,并运用粒子群算法迅速获得结构系统可靠性优化设计的初始点.并提出了一种小波神经网络的逆映射模型以优化设计参数,针对机械零部件的实验结果表明,上述方法行之有效.
針對多失效模式的結構繫統的可靠性優化問題,提齣瞭隨機模擬-小波神經網絡方法(MCS-WNN),將可靠性優化設計中的非正態隨機參數的概率約束轉化為等價的確定性約束,併運用粒子群算法迅速穫得結構繫統可靠性優化設計的初始點.併提齣瞭一種小波神經網絡的逆映射模型以優化設計參數,針對機械零部件的實驗結果錶明,上述方法行之有效.
침대다실효모식적결구계통적가고성우화문제,제출료수궤모의-소파신경망락방법(MCS-WNN),장가고성우화설계중적비정태수궤삼수적개솔약속전화위등개적학정성약속,병운용입자군산법신속획득결구계통가고성우화설계적초시점.병제출료일충소파신경망락적역영사모형이우화설계삼수,침대궤계령부건적실험결과표명,상술방법행지유효.
For the purpose of optimal reliability design of structural system with multi-failure modes, Monte Carlo Stochastic-Wavelet Neural Network(MCS-WNN) method is presented in this paper. The probability constraint is transferred into equivalent determinate constraint in optimal reliability design with non-normal random parameters. Thus, the initial design point in structural system for optimal reliability design can be obtained rapidly by Particle Swarm Algorithm. And then, an inverse mapping model of Wavelet Neural Network is presented to optimpze design parameters optimization. Experimental results for mechanical parts show that the above ementioned methods are effective.
