【机械类毕业论文中英文对照文献翻译】在神经网络中起重机传输遗传算法最佳化
【机械类毕业论文中英文对照文献翻译】在神经网络中起重机传输遗传算法最佳化,机械类毕业论文中英文对照文献翻译,机械类,毕业论文,中英文,对照,对比,比照,文献,翻译,神经网络,起重机,传输,遗传,算法,最佳,最好
附 录 AGenetic Algorithm Optimization On CraneTransmission In Neural NetworkAbstract:The fuzzy optimization mathematic model is established to design crane transmission. The method of second-class comprehensive evaluation was used by the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained, and the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of neural network algorithm is adopted to train feed-forward networks so as to fit relative coefficient. Then the fitness function with penalty terms is built by penalty strategy, neural networks program is recalled, solver functions of Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization mathematic model.Index :Terms-Crane Mechanism; Genetic Algorithm Optimization; Neural Networks.FUZZY OPTIMIZATION MATHEMATICAL MODEL OF TRACTION MECHANISMDesign sample: A involute helicoidal worm gearing is adopted in crane transmission, which has principal parameters as follows: rated power Pe=1.5kw, output speed 28.4r/min, output torque T2=295.87N.m, gear ratio u=49.3, working load factor k=1.05. The worm is machined and heat-treated 45 steel and the tooth corona of gear is made of machined ZQA1 9-4A. Specifying objective functionIn order to economize nonferrous metal of tooth corona of worm gear, the objective function should be specified that the volume of tooth corona of worm gear in tractionmechanism incline to minimum=1.5. According to Fig.1, d0, di2 and b are outer diameters, inner diameters and face width of tooth corona of worm gear respectively, thus the volume of tooth corona is;whereTherefore the objective function iswhere m-module of gear;d1-reference diameter of gear;z1-number of start of worm.Fig.1.the configure drawing of worm gearB. Selecting design variablesAccording to equation of the objective function, Z1 ,m,d1should be selected as design variables, that is:C. Establishing fuzzy constraintsConsidering the random character of the value of design parameters and some factors whose value is very indefinite such as loading property and material quality, the fuzzyconstraints are set up, including the property and boundary constraints.1)Limit of number of start of worm: to powerdrive, z1=12;2)Limit of module of gear:2m8;3)Limit of the lead angle of the worm for guaranteeingthe efficiency of the worm gearing:38,tan =mz1/d1;4)Constraint of contact strength of worm gear:Where Ze-the material elasticity factor, h-the contact stresses of worm gear;h-the fuzzy value of the allowable contact stresses of worm gear.5)Constraint of tooth beam strength of worm gear:Where f the beam stress of gear teeth; f-fuzzy value of the allowable bending stress of worm gear teeth;Yf -the profile factor for worm gear teeth.6)Constraint of the stiffness of worm:The worm is supported between two bearings, if the worm shaft bends too much, that is, the teeth will not mesh properly, and the result will be excessive wear and early failure. So the maximum deflection isWhere Ft1-the tangential force of the worm(N),F r1-the radial force of the worm(N),E-the modulus of elasticity(Mpa),I-the inertia moment of the dangerous cross-section of worm(mm4)L-the distance of the worm bearings (mm),L=0 .9muz1.IV.FUZZY OPTIMIZATION MATHEMATICAL MODEL OF TRACTION MECHANISMThe key of this method is how to decide the optimal level value. Several factors, such as factor class, factor fuzziness and the different influence of the factors on the different optimal level values, were considered and the method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value*of every fuzzy constraint can be attained, that is *=0.71.Therefore the fuzzy optimization problem is converted into the usual optimization problem.V.TRAINING RELATION COEFFICIENT BY NEURAL NETWORKSNeural networks are composed of simple element operating in parallel. These elements are inspired by biological nervous systems. As in nature, the network function is determined largely by the connections between elements. We can train a neural network to perform a particular function by adjusting the values of the connections(weights)between elements. Commonly neural networks are adjusted, or trained, so that a particular input leads to a specific target output based on a comparison of the output and the target, until the network output matches the target. Some points on relation curve between teeth number Z2 and the profile factor Yf of worm gear are selected as training sample data, the Fast Back Propagation are adopted to train feed-forward networks, the weights and biases of the network are updated. Then neural networks is simulated by the function of Neural Networks Toolbox in MATLAB. Program as follows:Z2=0:10:90;YF=2.58,2.5176,2.4566,2.3972,2.3392,2.2825,2.2273,2.1734,2.1208,2.0695n1=5;W1,b1,W2,b2= initff(Z2,n1,tansig,YF,purelin);fpd=100;mne=20000;sse=0.001;lr=0.01;tp=fpd, mne, sse, lr;W1,b1,W2,b2,te,tr=trainbpx(W1,b1,tansig,W2,b2,purelin,Z2,YF,tp)y=simuff(Z2,W1,b1,tansig,W2,b2,purelin)VI.SOLVING USUAL OPTIMIZATION MATHEMATICAL MODEL BY GENETIC ALGORITHM TOOLBOXOne key to successfully solving many types of optimization problems is choosing the method that best suits the problem. The Genetic Algorithm and Direct Search Toolbox is a collection of functions that extend the capabilities of the Optimization Toolbox and the MATLAB? numeric computing environment. The Genetic Algorithm Toolbox includes routines for solving optimization problems using Genetic algorithm Direct search. These algorithms enable you to solve a variety of optimization problems that lie outside the scope of the standard Optimization Toolbox. Firstly the fitness function with penalty terms is built by penalty strategy with addition type, and the fitness function is programmed in MATLAB language, and above neural networks program fitting the profile factor of worm gear teeth is recalled, then the nonlinear constraints function areprogrammed and the solver functions of Genetic Algorithm Toolbox are adopted. Program as follows:options= gaoptim set (PopulationSize,20);options=gaoptimset(Generations,100);options=gaoptimset(CrossoverFraction0.95, MigrationFraction0.01);options=gaoptimset(SelectionFcn, selection-tournament,CrossoverFcn, cross over scattered, Mutation Fcn,Mutation gaussian); nvars=3;lb=1;2;10;ub=2;8;150;x, Fval, exit Flag, Output=ga(fitnessfun, nvars,lb, ub, yueshufun, options)After function counting 108 times and iterating 326times, the final running output of above programming is:x1=1.0102,x2=4.8889,x3=78.2222,f(X)=1090628.VII.CONCLUSIONThis paper explored the methods available in the Genetic Algorithm and Neural Networks Toolbox. Compared with standard optimization algorithms(f(X)=1269257.5),the objective function optimum in the genetic algorithm is about16 .37%less than the former. Therefore we saw that the genetic algorithm is an effective solver for non smooth problems. Additionally, we found that the genetic algorithm can be combined with other solvers, such as fuzzy logic and neural networks, to efficiently find a more accurate solution.TABLE IOUTPUT OF STANDARD OPTIMIZATION AND GENETIC ALGORITHM附 录 B在神经网络中起重机传输遗传算法最佳化摘要:那失真的适宜数学模型在设计起重机传输建立。那方法的二等的综合评价被那最佳的把割集弄平整使用经由,那方法的二等的综合评价是使用经由那最佳的把割集弄平整,因此每个模糊约束那最佳的价值可以是获得弄平整,并且那模糊的最佳化是被变成那通常的最佳化。神经网络算法那背面加固增长的将采用到连续性前馈网络如此适合相关系数。然后那用罚款期限是构成由罚款策略装配功能、神经网络计划是召回、解算机功能的遗传算法工具箱的matlab软件是采用到解决那最佳化数学模型。索引词:起重机机构;遗传算法最佳化;神经网络。模糊的最佳化数学模型的牵引机构设计渐开线螺旋状的蜗轮传动装置是采民用在起重机传输,哪个有主参数如下:额定功率Pe=1.5kw、输出速度28.4rmin、输出转矩T2=2 295.87n.m、齿轮比U=49.3、工作负荷因素k=1.05, 那螺旋是机器和经加热处理材45钢和那由ZQA19-4构成的齿轮的齿轮冠.A指定目标函数为了节省有色金属的齿轮冠的螺旋齿轮,那目标函数将应指定那那大量的齿轮冠的螺旋齿轮在牵引机构向最小的按照图1倾斜,d0、 di2,b分别是外径、内径和螺旋齿轮的齿面宽冠,因此那是大量的牙齿冠;由所以那目标函数是m齿轮模数;d1齿轮分度圆直径;z1螺旋开始的齿数。图1.涡轮传动装置图B反面选择设计参数按照等式的那目标函数、,m、d1将应虽然设计参数选择,但是简而言之:C建立模糊约束认为值的随机特性设计参数和一些因素谁的价值很不定的比如负荷性质和材料品质、那模糊约束是建立、包括那性质和边界约束在内。1)极限的开始的螺旋的齿数:z1=12;;2)极限的齿轮的模数:2m8;3)极限的那导程角螺旋的因为保证蜗轮传动装置的效率:38,tan =mz1/d1;4)约束的接触强度的螺旋齿轮:那材料弹性因素、h那接触应力的螺旋齿轮;h-那模糊的值那容许接触应力的螺旋齿轮。5)约束的牙齿梁强度的螺旋齿轮:那横梁强调的轮齿;f-模糊的值那容许弯曲应力的螺旋齿轮牙齿;Yf-那轮廓因素因为螺旋齿轮牙齿。)约束稠的的的螺旋:那螺旋信息系统支持在.之间二轴承、如果那蜗杆轴弯曲多,那就是说,那牙齿不会适当地网孔,那么,那结果将要成为.的过度磨损和过早损坏所以极限偏转是 ft1-螺旋的切向力(N)、F r1-螺旋的径向力(N)、E-那弹性模数(Mpa)、I-危险截面的惯性矩的螺旋(mm4)L-蜗杆轴承的距离(毫米)、L=9muz1IV.模糊的最佳化数学模型的牵引机构这个的键方法是如何决定那最佳的把价值个别的因素弄平整、比如因素通信链路分析器系统、因素模糊和因素的不同的影响上去那不同的最佳的把价值弄平整、是认为和那方法的二等的综合评价是使用以那最佳的把割集弄平整为基础,因此那最佳的把价值的每模糊约束可以是获得弄平整、简而言之那模糊的优化问题是变为那通常的优化问题。V.连续性相关系数由神经网络神经网络由.组成简单的元件并行操作这个元件被生物学的神经的体系当做本质上鼓舞、那网络函数决意大量地由那关系在.之间元件我们可以训练一神经网络执行一特定函数由调整那值那关系(重量)在.之间元件通常神经网络是调整,否则连续性,结果一特别的输入导致一具体任务产量以一比较产量的和那靶子为基础、直到那网络产量相配那靶子。一些涨若干点相关曲线在齿数和那轮廓因素的螺旋齿轮被选为连续性样本数据之间、那背面加固繁殖将采用到连续性前馈网络、网络的重量和偏见是更新然后神经网络被那功能的神经网络工具箱在matlab模拟。计划如下:Z2=0:10:90;YF=2.58,2.5176,2.4566,2.3972,2.3392,2.28 25,2.2273,2.1734,2.1208,2.0695;n1=5;W1,b1,W2,b2= initff(Z2,n1,tansig,YF,purelin);fpd=100;mne=20000;sse=0.001;lr=0.01;tp=fpd, mne, sse, lr;W1,b1,W2,b2,te,tr=trainbpx(W1,b1,tansig,W2,b2,purelin,Z2,YF,tp)y=simuff(Z2,W1,b1,tansig,W2,b2,purelin)VI.解决通常的最佳化数学模型由遗传算法工具箱单密钥到成功地解决许多种优化问题是选择那方法那井衣服那问题那遗传算法和直接检索工具箱是许多功能那伸展那做.的能力那最佳化工具箱和那matlab?数字计算环境那遗传算法工具箱包括常规因为解决优化问题与罚款期限是用加法类型造由罚款策略一起使用遗传算法直接检索这算法使你解决种种的优化问题那谎言超出那标准最佳化工具箱范围。第一那适合功能,并且那适合功能是编制matlab语言、和在神经网络计划适合那轮廓因素的螺旋齿轮牙齿是召回上、然后那非直线型限制功能是程序和那解算机功能的遗传算法工具箱是采用。程序如下:选择权gaoptimset(总体大小 ,20);选择权gaoptimset(世代,100);选择权gaoptimset(交叉分数0.95, 分数0.01);选择权gaoptimset(s选择完全约束的非晶网、选择锦标赛、交叉分散、变化高斯型曲线);nvars=3;lb=1;2;10;ub=2;8;150;x,Fval, exitFlag, Output=ga(fitnessfun, nvars,lb, ub, yueshufun, options))在功能计算108次并重复次时以后,那最后的焊道上面程序的产量是:x1=1.0102,x2=4.8889,x3=78.2222,f(X)=1090628.VII.结论这个纸探测那方法有效范围那遗传算法和神经网络工具箱和.相比标准最优化算法f(X)=1269257.5)、那目标函数在遗传算法的最佳的是 大概16 .37%少于比前者.因此我们努力设法使那遗传算法是一有效的解算机因为nonsmooth问题.加之、我们发现那遗传算法可以与.化合其他的解算机、比如模糊逻辑和神经网络、有效地发现一更精确的解答。表格1标准最佳化的产量和遗传算法解算机X1X2X3F(X)标准最佳化1.05014.357478.45171269257.5遗传算法1.01024.888978.22221090628
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