共查询到19条相似文献,搜索用时 171 毫秒
1.
2.
3.
4.
身管结构的优化通常涉及到刚度、重量等多个目标,为了节省优化迭代过程中大量有限元计算的时间,以均匀试验的样本数据为输入,采用神经网络和遗传算法相结合的方法对火炮身管结构进行优化。采用模糊设计方法建立多个优化目标,通过权重将其转化成单目标,并在传统方法的基础上进行改进,采用权重优化的方法来确定权重,建立了基于神经网络和遗传算法的权重优化模型,从而克服了传统上确定权重方法主观性大的问题;在此基础上,进行基于神经网络和遗传算法的身管结构优化的求解。经算例验证表明,该方法可以获得较好的优化结果,并能大大提高身管结构优化的效率。 相似文献
5.
针对空气轴承式板形仪检测辊环的结构参数优化问题求解的复杂性,提出了采用基于神经网络的模拟退火遗传算法的智能优化方法.对板形仪检测辊环结构参数优化的神经网络基本结构、遗传算法个体与适应度函数的构造、遗传算子的设计及参数选取等进行了深入的研究,并结合工程实践有效验证了该算法的有效性. 相似文献
6.
密封条结构参数优化设计方法 总被引:4,自引:0,他引:4
为了对轿车车门密封条结构参数进行优化设计,采用遗传算法和神经网络相结合的策略,首先利用神经网络建立密封条结构设计参数与压缩负荷、应力等的非线性全局映射关系,获得求解结构优化问题所需的目标函数,然后用遗传算法进行优胜劣汰的寻优搜索运算,求出最优解。优化结果表明,椭圆形结构在壁厚为1.5mm、高度为20mm时,压缩负荷和应力能达到目标函数要求。压缩负荷和应力的优化结果与理论计算值的误差分别为7.4%、9%,因此,利用神经网络和遗传算法进行结构参数优化的方法是可行的。 相似文献
7.
基于BP-NSGA的注塑参数多目标智能优化设计 总被引:1,自引:0,他引:1
为获得成型性能最优的注塑参数设计方案,提出了基于BP神经网络和非支配排序遗传算法的注塑参数多目标优化方法。将注塑模结构尺寸参数和注塑工艺参数作为待优化的设计变量,建立了以高质量、低成本、高效率为优化目标的注塑参数优化设计模型。基于非支配排序遗传算法获取给定参数范围内的所有Pareto最优解,并通过建立多输入和多输出的BP神经网络来快速获得非支配排序遗传算法优化进程中所有个体的适应度值。开发了基于BP神经网络与非支配排序遗传算法集成的注塑参数智能优化设计系统,并通过鼠标注塑参数设计实例,验证了其适用性和有效性。 相似文献
8.
工时定额数据量大、影响因素多,使用常规拟合方法计算工时定额比较困难。为提高工时定额计算的正确性,采用人工神经网络技术,在MATLAB中建立了工时定额计算神经网络模型。针对BP神经网络存在易陷入局部最小值、收敛速度慢等不足,引入标准遗传算法来优化神经网络的权值和阈值。实验结果表明,基于实数编码的遗传算法优化速度快,优化后的神经网络迅速收敛,神经网络模型的测试误差低于5%。遗传神经网络可以克服单独使用神经网络时存在的缺点,训练好的模型在工时定额计算时正确性较高,有较好的实用价值。 相似文献
9.
针对滚动轴承的故障诊断问题,提出了一种基于遗传算法的BP神经网络滚动轴承故障诊断方法。以BP神经网络的误差为目标函数,利用遗传算法进行BP神经网络的权值和阈值优化,并用优化后的BP神经网络进行故障诊断。通过MATLAB仿真,结果表明遗传算法优化的BP神经网络相比传统的BP神经网络具有更好的诊断效率和准确度。 相似文献
10.
为提高大量程六维力传感器的测量精度,提出了一种新型的六维力传感器非线性静态解耦方法,该方法结合混合递阶遗传算法和小波神经网络的优点,采用递阶遗传算法与最小二乘法分别对小波神经网络隐层结构参数以及输出层权值进行优化,再将优化后的小波神经网络模型用于六维力传感器非线性解耦.建立了基于混合递阶遗传算法和优化小波神经网络的六维力传感器非线性解耦模型,设计了基于混合递阶遗传算法的小波神经网络结构及参数优化算法,给出了六维力传感器非线性解耦的具体实现流程.以最新研制的6-UPUR大量程柔性铰六维力传感器为对象进行实验,结果表明,采用该方法六维力传感器的Ⅰ类误差和Ⅱ类误差分别为1.25%和2.59%,比采用BP和RBF神经网络方法的测量精度高. 相似文献
11.
12.
为满足刚度大、强度高、质量小的设计要求,本文针对卫星天线臂的结构优化设计提出了分级遗传算法。首先,依据设计与制造要求,将天线臂结构优化设计分解为拓扑构型与杆件尺寸两级优化问题。然后,将单元材料相对密度作为基因,整体结构的相对密度作为染色体,将刚度与质量转化为适应度函数,形成拓扑构型的遗传算法。其次,在保持拓扑构型不变的条件下,将组成拓扑构型的各杆件的剖面面积作为设计变量,杆件结构的质量作为目标函数,形成杆件尺寸优化模型,通过引入遗传算子,形成第二级遗传算法。最后,给出了某卫星天线臂结构优化设计实例,证实了本文分级遗传算法的有效性。 相似文献
13.
14.
15.
16.
F. Jafarian M. Taghipour H. Amirabadi 《Journal of Mechanical Science and Technology》2013,27(5):1469-1477
Our goal is to propose a useful and effective method to determine optimal machining parameters in order to minimize surface roughness, resultant cutting forces and maximize tool life in the turning process. At first, three separate neural networks were used to estimate outputs of the process by varying input machining parameters. Then, these networks were used as optimization objective functions. Moreover, the proposed algorithm, namely, GA and PSO were utilized to optimize each of the outputs, while the other outputs would also be kept in the suitable range. The obtained results showed that by using trained neural networks with genetic algorithms as optimization objective functions, a powerful model would be obtained with high accuracy to analyze the effect of each parameter on the output(s) and optimally estimate machining conditions to reach minimum machining outputs. 相似文献
17.
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationaUy expensive simulation models.Existing metamodels main focus on polynomial regression(PR),neural networks(NN)and Kriging models,these metamodeis are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity.To address the problem,a reduced approximation model technique based on support vector regression(SVR)is introduced in order to improve the accuracy of metamodels.A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear.First appropriate design parameter samples are selected by experimental design theories,then the response samples are obtained from the simulations such as finite element analysis,the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions.Combining other constraints,the robust optimization model is formed which can be solved by genetic algorithm(GA).The applicability of the method developed is demonstrated using a case of two-bar structure system study.The performances of SVR were compared with those of PR,Kriging and back-propagation neural networks(BPNN),the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty.The robust optimization solutions are near to the real result,and the proposed method is found to be accurate and efficient for robust optimization.This reaserch provides an efficient method for robust optimization problems with complex structure. 相似文献
18.
Jean-Luc Marcelin 《The International Journal of Advanced Manufacturing Technology》2007,32(7-8):711-718
This work examines the possibility of using some classical optimization methods (augmented Lagrange multiplier method) or genetic algorithms and neural networks to optimize the cooling regime in hot-rolled complex beam processing. The objective is to optimize the cooling conditions so as to minimize the residual stresses. A detailed example shows that using this stochastic method and neural networks can be efficient. In other words, this work involves finite element analyses of both mechanical and thermodynamic properties of cooling beams, neural networks, genetic algorithms, and optimization to find a cooling regime with minimum residual stresses. 相似文献